{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T05:43:26.591305Z",
     "start_time": "2018-07-12T05:43:17.820110Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import GroupKFold\n",
    "import sys\n",
    "import os\n",
    "sys.path.append(f'/home/{os.environ.get(\"USER\")}/PythonLibrary')\n",
    "import lgbextension as ex\n",
    "import utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:03:04.044962Z",
     "start_time": "2018-07-12T06:02:13.934731Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:05<00:00,  3.98it/s]\n",
      "100%|██████████| 20/20 [00:01<00:00, 13.14it/s]\n",
      "100%|██████████| 20/20 [00:36<00:00,  1.81s/it]\n"
     ]
    }
   ],
   "source": [
    "train = utils.load_train()\n",
    "test = utils.load_test()\n",
    "prev = utils.read_pickles('../data/previous_application')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:03:04.048725Z",
     "start_time": "2018-07-12T06:03:04.046481Z"
    }
   },
   "outputs": [],
   "source": [
    "# subset\n",
    "#prev =prev[prev.NAME_CONTRACT_STATUS=='Approved'].drop('NAME_CONTRACT_STATUS', axis=1)\n",
    "\n",
    "#prev = prev.sort_values(['SK_ID_CURR', 'DAYS_DECISION'], ascending=[True, False]).drop_duplicates('SK_ID_CURR', keep='first')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:03:18.211958Z",
     "start_time": "2018-07-12T06:03:04.049915Z"
    }
   },
   "outputs": [],
   "source": [
    "# colsample\n",
    "\n",
    "col = [c for c in prev.columns if c.startswith('app_')]\n",
    "prev.drop(col, axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:03:35.864072Z",
     "start_time": "2018-07-12T06:03:35.851259Z"
    }
   },
   "outputs": [],
   "source": [
    "col_cat = [c for c in prev.columns if prev[c].dtype=='O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:03:44.871436Z",
     "start_time": "2018-07-12T06:03:36.028230Z"
    }
   },
   "outputs": [],
   "source": [
    "prev = pd.get_dummies(prev, columns=col_cat, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:03:49.421247Z",
     "start_time": "2018-07-12T06:03:44.872792Z"
    }
   },
   "outputs": [],
   "source": [
    "tmp = pd.merge(prev, train[['SK_ID_CURR', 'TARGET']], on='SK_ID_CURR', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:04:05.749340Z",
     "start_time": "2018-07-12T06:03:49.422701Z"
    }
   },
   "outputs": [],
   "source": [
    "prev_tr = tmp[~tmp.TARGET.isnull()].reset_index(drop=True)\n",
    "prev_te = tmp[tmp.TARGET.isnull()].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:04:06.022651Z",
     "start_time": "2018-07-12T06:04:05.751065Z"
    }
   },
   "outputs": [],
   "source": [
    "tmp  = prev_tr[['SK_ID_CURR']].set_index('SK_ID_CURR')\n",
    "tmp['y'] = prev_tr.TARGET.values\n",
    "tmp['cnt'] = prev_tr['SK_ID_CURR'].value_counts()\n",
    "tmp['w'] = 1 / tmp.cnt.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:04:13.437558Z",
     "start_time": "2018-07-12T06:04:06.024315Z"
    }
   },
   "outputs": [],
   "source": [
    "X_tr = prev_tr.drop(['SK_ID_PREV', 'SK_ID_CURR', 'TARGET'], axis=1).reset_index(drop=True)\n",
    "tmp.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:04:13.441900Z",
     "start_time": "2018-07-12T06:04:13.438979Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1413701, 439)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:04:13.447496Z",
     "start_time": "2018-07-12T06:04:13.443043Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1413701, 4)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:04:13.457219Z",
     "start_time": "2018-07-12T06:04:13.448584Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>y</th>\n",
       "      <th>cnt</th>\n",
       "      <th>w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1413696</th>\n",
       "      <td>443544</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>0.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413697</th>\n",
       "      <td>209732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413698</th>\n",
       "      <td>249544</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413699</th>\n",
       "      <td>400317</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413700</th>\n",
       "      <td>261212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SK_ID_CURR    y  cnt         w\n",
       "1413696      443544  0.0   22  0.045455\n",
       "1413697      209732  0.0    6  0.166667\n",
       "1413698      249544  0.0    3  0.333333\n",
       "1413699      400317  0.0    9  0.111111\n",
       "1413700      261212  0.0    9  0.111111"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:04:13.464529Z",
     "start_time": "2018-07-12T06:04:13.458385Z"
    }
   },
   "outputs": [],
   "source": [
    "SEED = 71\n",
    "\n",
    "param = {\n",
    "         'objective': 'binary',\n",
    "         'metric': 'auc',\n",
    "         'learning_rate': 0.2,\n",
    "         'max_depth': 6,\n",
    "         'num_leaves': 63,\n",
    "         'max_bin': 255,\n",
    "         \n",
    "         'min_child_weight': 10,\n",
    "         'min_data_in_leaf': 150,\n",
    "         'reg_lambda': 0.5,  # L2 regularization term on weights.\n",
    "         'reg_alpha': 0.5,  # L1 regularization term on weights.\n",
    "         \n",
    "         'colsample_bytree': 0.9,\n",
    "         'subsample': 0.9,\n",
    "#         'nthread': 32,\n",
    "         'nthread': 64,\n",
    "         'bagging_freq': 1,\n",
    "         'verbose':-1,\n",
    "         'seed': SEED\n",
    "         }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:04:13.525383Z",
     "start_time": "2018-07-12T06:04:13.465764Z"
    }
   },
   "outputs": [],
   "source": [
    "fold = 5\n",
    "group_kfold = GroupKFold(n_splits=fold)\n",
    "tmp['g'] = tmp.SK_ID_CURR % fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:04:13.531132Z",
     "start_time": "2018-07-12T06:04:13.527126Z"
    }
   },
   "outputs": [],
   "source": [
    "dtrain = lgb.Dataset(X_tr, tmp.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:06:16.756774Z",
     "start_time": "2018-07-12T06:04:13.532701Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50]\tcv_agg's auc: 0.652086 + 0.00257234\n",
      "[100]\tcv_agg's auc: 0.653304 + 0.0025141\n",
      "[150]\tcv_agg's auc: 0.653134 + 0.00262983\n",
      "[200]\tcv_agg's auc: 0.652429 + 0.00272999\n"
     ]
    }
   ],
   "source": [
    "ret = lgb.cv(param, dtrain, 9999, folds=group_kfold.split(X_tr, tmp.y, tmp.g), \n",
    "             stratified=False,\n",
    "             early_stopping_rounds=100, verbose_eval=50,\n",
    "             seed=SEED)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:06:16.772907Z",
     "start_time": "2018-07-12T06:06:16.764060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.653550052295366"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret['auc-mean'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T05:56:08.888562Z",
     "start_time": "2018-07-12T05:54:12.634792Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50]\tcv_agg's auc: 0.683551 + 0.00116634\n",
      "[100]\tcv_agg's auc: 0.68464 + 0.00125563\n",
      "[150]\tcv_agg's auc: 0.683401 + 0.000969399\n"
     ]
    }
   ],
   "source": [
    "dtrain = lgb.Dataset(X_tr, tmp.y, weight=tmp.w)\n",
    "\n",
    "ret = lgb.cv(param, dtrain, 9999, folds=group_kfold.split(X_tr, tmp.y, tmp.g), \n",
    "             stratified=False,\n",
    "             early_stopping_rounds=100, verbose_eval=50,\n",
    "             seed=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:38:09.502299Z",
     "start_time": "2018-07-12T06:37:59.559574Z"
    }
   },
   "outputs": [],
   "source": [
    "model = lgb.train(param, dtrain, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:38:09.520548Z",
     "start_time": "2018-07-12T06:38:09.508569Z"
    }
   },
   "outputs": [],
   "source": [
    "imp = ex.getImp(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:45:17.453555Z",
     "start_time": "2018-07-12T06:45:17.444333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>split</th>\n",
       "      <th>gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMT_GOODS_PRICE-d-total_debt</td>\n",
       "      <td>90</td>\n",
       "      <td>15238.959805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMT_ANNUITY-m-app_AMT_GOODS_PRICE</td>\n",
       "      <td>195</td>\n",
       "      <td>11344.598140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DAYS_LAST_DUE_1ST_VERSION-m-app_DAYS_BIRTH</td>\n",
       "      <td>67</td>\n",
       "      <td>10518.170499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMT_ANNUITY-m-app_AMT_CREDIT</td>\n",
       "      <td>198</td>\n",
       "      <td>7845.242754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAYS_DECISION</td>\n",
       "      <td>121</td>\n",
       "      <td>5987.397496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      feature  split          gain\n",
       "0                AMT_GOODS_PRICE-d-total_debt     90  15238.959805\n",
       "1           AMT_ANNUITY-m-app_AMT_GOODS_PRICE    195  11344.598140\n",
       "2  DAYS_LAST_DUE_1ST_VERSION-m-app_DAYS_BIRTH     67  10518.170499\n",
       "3                AMT_ANNUITY-m-app_AMT_CREDIT    198   7845.242754\n",
       "4                               DAYS_DECISION    121   5987.397496"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:39:31.812683Z",
     "start_time": "2018-07-12T06:39:28.548532Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mBttv2Z5Gms64GSGEEEJYQAQ1IXRT/pZ+N+AySTOBk0nTzARg+03SyMOJwE3daGoiaX0JtifktTh3AH2A6cAGklar1BkITMp1W8sMSauTLsifbNDeAGBy7Y3tZ/N0t32Bf5NGVBqprc/ZzvYllfRtgK2B/5bUv8F+blcnvSFJd+Ug77JadztYboHsDra1KzAI+JjtbYFHgVU62NV6/VoOeKkI1GrrlhrVaRSEhhBCCMusCGpC6L4vAlfb3sB2i+31gRnAekWZ84FTbM/pRjvfA36g4o5mpICmtnblKuD/JC0P6Q5ipEXwfwDuBVbNaeQy5wNX2n692pCkbYD/D7g4v99L0or59fuBtYBZXd2RPE3ve8ApDfbz3NwOklZWkzvC5e3tmYOBI4ApwIaSaut4hjYoB/AK6eYKAA8Dn1C6Q93yud4fc96/avsPvBv4h+3X89qXHTq426OA/ST1ycHnZ3OfXgZmSBqS91eSti3qDZG0XN6fDwFPVPodQgghLPMiqAmh+4YCt1TSbqK4Y5btibav6k4jtn9HugvZHUq3OX6QtEbnrlzkW8AbwFRJ00h3WtvPGbAf6QJ5GjA1ly3v6rWz8i2dScHMsbbvzXl7AI/nKVd3kaaAdWXqWOkSYBdJG9bZz4uBeyRNJI1yrVAUKdfUjMsL58v6bwBHArfnGwX8pUkfLiUdz/ts/410DO8DxgNjbf+mKPdYXsNzJ7BCnkZ3BmkKWrtsjyVNPxxH+nzcX2QfBHwlH9+JwL5F3hOk4OoO4Ki8f/cBWzS7UUAIIYSwLFG61gkhhLC0am1tdVtb2+LuRgghhICkMbZb2y/ZOTFSE0IIIYQQQujVVmi/SAjhP0nSMNLUsdKNts+qV35xkbQn6RbIpRm291sc/VlS5BtH3Fsna/durqkKIYQQQgMx/SyEEJZy/Tfo57u/ddLi7kYIITS0zlFN7wcTliIx/SyEEEIIIYQQ6oigJoQQQgghhNCrRVATQgghhBBC6NUiqAlLFEn7SXJ+qCGSWvL7M4oya0v6l6SLJA0rnlcyv3jddHKupPGSrqukXSlplqSVi3ZmVvrxtaL8RZIOy69HSmot8lokPZ5f7yrpNklfLvr3pqQJ+fWNkqZK6lPUv13SgV0/kv9ZkrbPx2f3Im2FnHZFkbaSpBcljZB0RIPj0fCGCJI+LekRSVNy2etqDyPND608VdL0fDzvlbR5UXcNSdfk/Cfz+V49520saW5+Ts9kSQ9LOqSou66k3+XPzSRJv23Sx9q2xuWyF+e+lemTc/sr5DqDJI0otvEZSWNy/SmSvp/Tz8yf0fI5PfEQzhBCCMu8CGrCkmYo8CegvKB/CtineD+E9IBCbJ+VnxDfH5hbe237R40ayBe6y5Ee/PiuSvZ84PAGVZ8Hvq7Kwx47yvYVRV+fBT6Z3w8BbgaG5f4NBla0Pbwr7SwmtfM2tJL+MjCwFigCewJPA9i+rDgezwM75/fD6jUgaVvgQuBg25sBA0gPs9wgF/k6sD2wte1NgB8AtxZtXwFMsb2x7Y2AZ4CfFU08YXuA7c1JD8P8RhHYnAncbntb21sA32nneDyR92tboD/w2Ur61sCGwBea7OfQ3NZWwMyiyHnF57y/7Vfa6UsIIYSw1IugJiwxJPUFdgK+woJBzVxgcjEScgBwQzea+hLwS+Bu4HOVvAuB42vfoFe8QLpV76HdaLuR04EhkvoD5wBH1ysk6RlJZ0kanUcsBkq6O488fLVBnX3zyMOjuew6Of1MSVdJuk/SNEmH5/RBOW1EOdLQqOOSliNdnB8K7F0J+gzcBeyd3w8FrqNrvgmcYfsJACcjbD+Q878BHG17bs6/A3gEOFDSpqTg4Oxie6cBO0lqqTZkezpwIlAb8VuXFATV8h/rSIdt/wt4CNi4kv7v3LcP1ql2St7PqbWytn/akfZKko6U1Capbc6rr3a2egghhNCrRFATliSDgTvzxdyLkgYWecNJF6frkUZTnu1GOweQvuG/joVHFp4mjTgcUq2UnQOcKGn5brS/ENuvAycBo4Dhtqc1KT7T9g7AaOByYD9gR+CMBuVHATvYHkAaETqxyNuaFHDsBJwu6X05/aPAcTl/c2DfJv3ZhTQC8hTwALBXJb927lbN2xrTZFvNbAmMrZchaU3S6NZfKlltud6WwKO236pl5MBiPLBFg/bGApvl1xcBV0n6g6RvS1q3Ix3OI4G7ARMq6X1Io0p31am2Fc2P0cnF1LN7GhWyfantVtuta/Xt25HuhhBCCL1WBDVhSTKUdAFM/l0GHHcCn8pp13e1AUnbAy/ki997SVOj1qgUOxs4mTp/H7ZnAH8mjfYskFWnuU49BMr2rcBLwE/aKVpbzzEBGG37NdvPAW/l0a6qfsDdkiYAJ5Au8GtG2H7D9vOk4Gf7nD7a9kzb80nn4uNN+tPsvGF7LLBJTr+1nX3rEEnr5Iv6aZKOA0T9411Lby+/bjO1F7Z/B2xECiK3AB5VeshmI5tKGgfcD9xi+/eV9DnAdNsTm2yjkXL62aAu1A8hhBCWOhHUhCVCvkDcDbhMaXH+yaQRFQHYfpP07fWJwE3daGoosFlu40lgdSrrGvLUo3HA/g22cTZpilD59zMHKIOjNYHZXejfW/kHAEn35Iv3S4oy84qy8yp1V5B0Tq7TltMvBi6wvTXwP8AqRZ3qBb3bSV+ApBVJI0Wn52N6IfCZOmuVbgPOpetTzyCtoxoIYPv5vDblcqCv7TnAfEn9KnUGApNqdfNUuVrflyeNRE1u0N6AMs/2HNu/sn0w6fPRLNB7IgcdA22fUU0nTUf7hKRPN9jP7ZpsO4QQQggVEdSEJcUXgattb2C7xfb6wAxgvaLM+cAp+QK20/IF7RBgm9xGC2laVXUKGsBZpOlgC7E9hXShXN68YCRwcLH25FDgvq70s9LWoHxxfFQn6nwz16mtQXo3MCv3rboeaLCklSWtDexMmq4FsIOkfvnCf3/SlLx69gAesb1+Pqb9SKMx1bVKlwGn2m4UQHTEucB38/qYmlWL1+cBP5a0CoCkPUnT6K4vztk3i/Knkkekqg1J+lBte/n97nnKGEp3TNuQfMODrrD9LPCt/FN1LvAdSRvn9paXdEJX2wohhBCWBRHUhCXFUOCWStpNwLdrb2xPtH1VN9rYBZhle1aRNgrYorpGIk8Lqrt+IzuLBQOuS4FXgPGSxgN9SXffWhKcRjq2fwSeq+Q9AtxBWsx+ap7GBvAgKYicAEzlnSlvVY3O2wLT82w/bfuiLva/to1HSdPnrpX0hKQHSCMetalvF5JGUB6XNJUUwHzO9hs5/zBgq3xThSeBFuDIoolN880UpuRtnm/7lzlve2CspMdIx+anuT/d8WtgTUkfq7OfJwE3SJpMOgfvLYqUa2rGSVq/m/0IIYQQej3ZnZr2H0JYSkg6E5ht+8JK+iDgGNuDF0/PQk9rbW11W1tb+wVDCCGERUzSmGI2SY+JkZoQQgghhBBCr1bvWRwh9HqShpHWz5RutN3wafXLGtt1HyBp+x5goVsF5xsPVP/N+JLtST3ZL0lHAMdUkkfZPrZe+cUlP1Poykry67Z3XAzdCSGEEJZpMf0shBCWctv2e5/vOOWgxd2NEMJS4gNH/9/i7kLoxWL6WQghhBBCCCHUEUFNCCGEEEIIoVeLoCaEEEIIIYTQq0VQsxSQND8/r2KipPGSTiifnJ7L/FDSrFq6pC0lTa09UDCn3S7pQEnvk3Rb3tYkSb9r0naLpMcb5K0gabak71XS98nPA6lt//9JGlY8d2N+8bru4nBJp+X9GSdpmqSbJW1R5M/MD5Ssvd9V0m359WGSXqg862OLeu3k8ndKeqlWv6f2I/fpoTrH7DlJ60q6UtKMYhsP1un/FEnHF/U3lTQy502WdGl1//P7wZIey/UnSBpc5F2Zj+3K+f3akmY2Oj5LmuK4jc+f8aslfbBSZoAkKz2gEyV/krR3UWZ/SXfm18Py39dj+dh+tEn7I5Weo1M7B0cWeW9/LovPx+OSbpX0HklbF+f7xeL831Pvby3/HdR9SGwIIYSwLIm7ny0d5truDyBpHeBa0lPkT81pywH7AX8lPYBypO2Jkm4GhpGeXj4YWNH2cEk/A35v+4e5/jZd7NcewBPA/pK+bduSViQ9qPIjtp/JF84ttp8gPdASSa/W9qcdF9j+Qa5zAPAHSVvbfqEDda+3Xb3DViPnkZ5c//9qCT20H6OA9SS1FE+1HwQ8bvtvkgBOtv3rRv2XtBbwhKRf2/4r8CPScflN7sPW1YqStiU9GPRTtmdI2hD4vaSnbD+Wi80HDgd+2oHjsyQ62favlQ7iccB9kray/WbOHwr8Kf++K382jwJulHQfsDzpPO6l9HDMfYCBtufloGSldto/yHabpDWBJyVdWbRdU/7dXgUcne/OV0u7Eritdv4ltXT9cIQQQghLtxipWcrYfp70lPRj8gUdwCeBx0kXqEOL4qcDQ5RuTXsOcHROXxd4ptjmY3TNUOCHwNPADjltNVIwPSdve14OBLrF9vXA3VSeZN8TbN8LvFJJ7vZ+2H4LuBE4oEg+ELiuE9uYA0wnnTNY+NxNqFPtJOBs2zNymRnA94CTizIXAsdLavrFRx4V+amk+yQ9JekTkn6RRyiubFCnRdL9ksbmnx1z+q6SRkm6RWnk6xK9M7L4qqTzc/l7Jb23A4cHJxcAfwf2ztsS8EXgMGAPSavkso8DtwKnkL4QuNr2k6RjOtv2vFxutu1nO9I+0Bd4jRQkNvMQ8MF2ynSKpCMltUlqm/Pq3J7cdAghhLDEiaBmKWT7KdK5XScnDSVdKN8C7JNHGbD9OukCdxQw3Pa0XP5i4PJ8oTpM0gc62welaW27A7fltofmNl8Efgv8RdJ1kg5SZapcN4wFNutg2QO04PSzPu1XeUcP7sd1pECGPNrzaeCmIv+8oo+/qlaW1A9YBagFnheQRqzukHS8pPfUaXNLYEwlrS2n1zxNGsk4pAP7sAawG3A8KSi4IG9r6xwwVz1PGiUaSAroflTkfQQ4Edga2Aj4fE5/FzA21/kjeRSyE8rPxk7AjBywjCQd85r/JQXGewPn5rS7gfWVprL9RNInOtDeryQ9RhqpPMN2w6BG0vKkv5XfdmC7G5WfW+CoRgVtX2q71XbrWn079fEOIYQQep0IapZeApC0EumibYTtl4GHSdPCALB9K/AS8JMi7S7gQ8DPSReCj3b0m/HCPsB9OXC6CdgvX7xh+wjSRdyfSUHVL7qyg3WoeF3vAUxl2vW2+xc/nf4quyf2w/YjQF9Jm5IupEfb/kdR5OSij+WDRg6QNBF4Cvih7Tfy9q4ANieNAO0KjM7BUkksfHzqpZ1NGr1p79+JW50eeDUBeM72hDwKNRFoqVN+ReDnkibkfpbrmf5s+6kcBFwHfDynvwVcn19fU6R3VPnZGAoMz6+HU4xe2n4tt/PLYmTmVWA70gjoC8D1kg5rp72DbG8D9ANOkrRBnTJ9cmAyB1gT+H0H9uPJ8nMLXNKBOiGEEMJSL4KapZCkD5GmuzwP7EVaXzNBabH3x1lwChqkC8a3ygTbL9q+1vYhwCOktTidMRQYlNscA6xFmgZX2/6EPC3oU8AXOrntRgYAk/PrOaQRhJo1gdk91M7bemg/hpNGazoz9ex621sCOwPnS3p/0adnbf/C9r7Av4GtKnUnAtWHXg0EJpUJtqcD44D9a2mSzipGCWrm5d9vFa9r71eQtF8xutBKGtF5Dtg296Ncn1INrBo9HbizTw0eAEzOgfUXgO/mz+aPgb0lrVbpd/XvYb7tkbZPBY6hg+c6r+8aC9S7sUBtTc0GpGNwdJ0yIYQQQuiACGqWMnlE5RLgovzt+VDgCNsttluADUnrCFZtso3davn5Ym8j0nSkjvZhdVLw1K9o92hgqKS+knYtivcH/tKJXWzU5hdII1C1oGAkeepUvpA9GLivu+0U7fXkflxH6t9udGwK0ttsPwT8Evh67tdetemFOdBZC5hVqfYD4Fu1hef597eB8+s0cRZpFKrW3rBilKCjfbylGF1oIwXZf8ujOYeQFuXXfETShnkq3wGkKXCQ/q36Yn79pSK9KSXHktbF3Em6EcN42+vnz+YGpJFyppjGAAAgAElEQVTEwU22samkDxdJHT7X+e9oAPBkozK2/wkcSxrRWbEj2w0hhBDCgiKoWTr0yd+CTwTuIa0B+N98QbUncHutYJ5e8yfgs022tx3QltcEPARclqdJNbKppGdqP6S7hP2hNn0n+w3wOdIF7DeUb3lLWsNwWCf3t+b4vN/TyEGB37nz2RnAxpLGA4+SFtNfU9StrqnZsVEjku4nTZPaPe/jnqTpTD2yH7YnAa+TjtlrlezzKv2sd9et7wNfzgHoHsDjeb/vIk1f+3ulvXGkxfC3SppCWgfzjZxe7dtE0khDT/oJcKik0cAmpIX0NQ+RblrxODCDtA6MXGZLSWNIwd/p7bRxXj4GU4HtgU/mu48NLbZZcxPNbzDRF7hK6eYFj5Gmy53WTvu/yp+LMcCVtqtrmBZg+1FgPHl9VQghhBA6R+nL/BBCWLzyyNdJtvepk/eq7b7/+V4tHVpbW93W1ra4uxFCCCEgaYzt6jT4bouRmhBCCCGEEEKvFg/fDB2i9BDHX1aS59lu+GT1Hmp3GDCkknxjfkhhT7azSPfvP7UfvZntkaS1UPXyFhqlkXQx6fbMpR/mO8AtcpJuIa1RK52S7x4YQgghhP+gmH4WQghLua36vce/PqUjj9cJIdRsdvRvFncXQlgqxfSzEEIIIYQQQqgjgpoQQgghhBBCrxZBTQghhBBCCKFXi6AmhAYk7SfJkjbL71vy+zOKMmtL+pekiyQNK54nM794fWw77YyXdF0l7UpJsyStXLQzs9KPrxXlL5J0WH49UlJrkdci6fH8eldJt0n6ctG/NyVNyK9vlDRVUp+i/u2Ses3zUyRtn4/P7kXaCjntiiJtJUkvShoh6YgGx6PhjRwkfUbSmPz8mimSvp/Tz8znblzO27+oc42kGUVb9+f0IyS9IOlRSdMk3Slph0q9wZJ+m+tNl/TPYjuL9IYdIYQQwpIugpoQGhtKelBpeUH/FFA+R2UIMBHA9lm2+9vuD8ytvbb9o0YNSNqc9He4i6R3VbLnA4c3qPo88PUGD+Nsl+0rir4+S3o4ZX/bQ4CbgWG5f4OBFW0P70o7i0ntvA2tpL8MDKwFiqQH0z4NYPuy4ng8D+yc3w+r14CkbYELgaG2twC2AmYWRc7L2/o88HNJyxd5xxefjZ2L9F/ZHmD7w8APgN9I2qRs1/bn8naPAu4rtvNwxw5NCCGEsHSKoCaEOiT1Jd0u+CssGNTMBSYXIyEHADd0o6kvkW4lfTfwuUrehcDxkurdev0F4F7g0G603cjpwBBJ/YFzgKPrFZL0jKSzJI2W9IikgZLulvSkpK82qLOvpIfziMTdktbJ6WdKukrSfXmk4vCcPiinjcijHhdLUqOOS1oO+ALpuOxdCfoM3AXsnd8PBa6ja04BzrA9FcD2v23/tFrI9hTgX8C7O7Nx2/cAlwN1j2NHSDpSUpuktn+8+mZXNxNCCCH0ChHUhFDfYODOfNH6oqSBRd5w4EBJ65FGU57tRjsHANeTLq6rIwtPk0YcDmlQ9xzgxMooQLfZfh04CRgFDLc9rUnxmbZ3AEaTLsL3A3YEzmhQfhSwg+0BpBGhE4u8rUkBx07A6ZLel9M/ChyX8zcH9m3Sn12AKbafAh4A9qrk187dqnlbY5psq5mtOlJX0vbA47ZfLJIvKKaNXd2k+lhgsy72D9uX2m613bpG3y4N6IUQQgi9RgQ1IdQ3lHQBTP5dBhx3Ap/Kadd3tYF8wfuC7b+QRl0GSlqjUuxs4GTq/K3angH8mTTas0BWneY69UAq27cCLwE/aafob/PvCcBo26/Zfg54K492VfUD7pY0ATgB2LLIG2H7DdvPk4Kf7XP6aNszbc8nnYuPN+lPs/OG7bHAJjn91nb2rTtOljQVeBA4rZJXTj/7rybbaDgiFUIIIYQFRVATQoWktYDdgMvy4vyTSSMqArD9Julb+hOBm7rR1FBgs9zGk8DqpKlTb7M9HRgH7L9Q7eRs0lSo8m95DlAGR2sCs7vQv7fyDwCS7smjC5cUZeYVZedV6q4g6Zxcpy2nXwxcYHtr4H+AVYo61cDL7aQvQNKKpJGi0/MxvRD4TJ21SrcB59L1qWeQ1lFt1yT/PNubAAcBVxfreDpjADC5K50LIYQQljUR1ISwsC8CV9vewHaL7fWBGcB6RZnzgVNsz+lKA3ntxxBgm9xGC2laVXUKGsBZpOlgC8lrNiax4M0LRgIHF2tPDgXu60o/K20NyqMLR3WizjdzndoapHcDs3LfquuBBktaWdLawM5ALRDaQVK/PM1uf9KUvHr2AB6xvX4+pv1IozHVtUqXAafa7k7AcC7wHUkbA0haXtIJ1UK2byCNYh3cmY1L+iTpJhGXd6OPIYQQwjIjgpoQFjYUuKWSdhPw7dob2xNtX9WNNnYBZtmeVaSNAraQtG5Z0PZE0vqKRs5iwYDrUuAVYLyk8UBf0t20lgSnkY7tH4HnKnmPAHcAD5GCjlr+g6QgcgIwlXemvFU1Om8LTM+z/bTti7rY/9o2HiUFmjdImpz79t4GxU8nrX2qBZnlmppxxZqog/L7qaTRt8G2n+hOP0MIIYRlhexOTbUPIYQeJ+lMYLbtCyvpg4BjbA9ePD1bOrS2trqtra39giGEEMIiJmlMMYOjx8RITQghhBBCCKFXq/f8ixBCD5I0jLR+pnSj7YZPq1/W2P5Og/R7gHuq6fnGA9V/v75ke1JP9kvSEcAxleRRto/tyXZCCCGE0D0x/SyEEJZym2/wHv9iWLM7YYfQsz525G2LuwshhCVUTD8LIYQQQgghhDoiqAkhhBBCCCH0ahHUhBBCCCGEEHq1CGpCCEsMSfPzs1omShov6YT8oNKyzA8lzaqlS9pS0lRJfYoyt0s6UNL7JN2WtzVJ0u+atN0iaa6kRyVNlvRnSYcW+YdJeqHyjJktct4mkn4naXque0Nue1dJt+UydfuS2328aOfjue0p+efIIu80Sa9LWqdIe7U7xzyEEEJYGsTdz0IIS5K5tvsD5Av3a4F3A6fmtOWA/YC/kh5gOtL2REk3A8OA70gaDKxoe7iknwG/t/3DXH+bdtp/0vaAXPZDwM2SlrN9Rc6/3vYCd0OTtApwO3CC7Vtz2idZ+GGcp7fXF0nvz/s82PZYSWsDd0maZfv2XGw2cCLpAZ0hhBBCIEZqQghLKNvPA0cCx0hSTv4k8DjwU2BoUfx0YIik/sA5wNE5fV3gmWKbj3Wi/aeAE4D2bt/8JeChWkCT695n+/FKuY705WjgSttjc5nZwDeAbxZlfgEcIGnNZp2SdKSkNklt/3j1zXZ2IYQQQujdIqgJISyxcmCxHFCbbjUUuA64BdhH0oq53OvAScAoYLjtabn8xcDlku6TNEzSBzrZhbHAZsX7AyrTz/oAWwFjOrCtjvRlyzrbasvpNa+SApuvN2vM9qW2W223rtF3pQ50L4QQQui9IqgJISzpBCBpJeDTwAjbLwMPA3vUCuWRkpeAnxRpdwEfAn5OCk4elVSdFtZu24XrbfcvfuZ2dEMd7IuAeg8Pq6b9CDhU0uodbT+EEEJYmkVQE0JYYuV1LfOB54G9SOtrJkiaCXycBaegAbyVf95m+0Xb19o+BHiEtBanowYAk9spMxHYriMb60BfJgLVB5JtB0yqbOcl0tqb/+lIuyGEEMLSLoKaEMISKY9iXAJcZNukAOYI2y22W4ANgT0krdpkG7vV8iWtBmwEPN3B9luAHwA/bqfotcCOkj5T1N1L0tZd6MvFwGF5bRCS1gK+D5xbp93/A/4fccOXEEIIIYKaEMISpU/tls7APcDdwP/mYGBP0l3GALD9GvAn4LNNtrcd0CbpMeAh4DLbjzQpv1Htls7ADcCPizufwcJranbMU9D2Ab4maZqkScBhpNGlTvXF9t+Ag4GfS5oCPAj8orwJQVF2Nmlt0cpN9ieEEEJYJih9ARpCCGFp1dra6ra2tsXdjRBCCAFJY2xXp1p3W4zUhBBCCCGEEHq1mIsdQlim5LUuv6wkz7P90cXRnxBCCCF0XwQ1IYRliu0JQP/F3Y//pJdnT+Ouyz+9uLsROmHPr/xucXchhBB6lZh+FkIIIYQQQujVIqgJIYQQQggh9GoR1IQQQgghhBB6tQhqwjJL0n6SLGmz/L4lvz+jKLO2pH9JukjSsOL5JPOL18e20854SddV0q6UNEvSykU7Myv9+FpR/iJJh+XXIyW1Fnktkh7Pr3eVdJukLxf9e1PShPz6RklTJfUp6t8u6cCuH8n/LEnb5+Oze5G2Qk67okhbSdKLkkZIOqLB8TirnbZul3R/Je1MSa9KWrtIe7XSj+8Xed+U9J38+hpJgyv9fim/3jj36dNFX1+V9ER+fYWkGZLWKepfKumkrhzHEEIIYWkSQU1Ylg0lPbyxvKB/ivQgxZohwEQA22fZ7m+7PzC39tr2jxo1IGlz0t/ZLpLeVcmeDxzeoOrzwNclrdSpPcpsX1H09Vngk/n9EOBmYFju32BgRdvDu9LOYlI7b0Mr6S8DA2uBIulhnU8D2L6sOB7PAzvn98MaNSJpLWBr4H2S+lWyXwSOb1B1LrC/pDU7s1M1tn9X9HUccEB+/2XgfODc3L/tgY8AF3alnRBCCGFpEkFNWCZJ6gvsBHyFBYOaucDkYiTkANKT5bvqS6TbB98NfK6SdyFwvKR6dyF8AbgXOLQbbTdyOjBEUn/gHODoeoUkPSPpLEmjJT0iaaCkuyU9KemrDersK+lhSY/msuvk9DMlXSXpPknTJB2e0wfltBGSJkm6WJIadVzScsAXSMdl70rQZ+AuYO/8fihwHV33RWAEcD3pc1C6DDhI0nvq1HsT+AXw9W603chPgC0kfQK4CDja9r/rFZR0pKQ2SW3/fOXNRdCVEEIIYckRQU1YVg0G7rQ9FXhR0sAibzhwoKT1SKMpz3ajnQNIF8XXsfDIwtOkEYdDGtQ9BzhR0vLdaH8htl8HTgJGAcNtT2tSfKbtHYDRwOXAfsCOwBkNyo8CdrA9gDQidGKRtzUp4NgJOF3S+3L6R4Hjcv7mwL5N+rMLMMX2U8ADwF6V/Nq5WzVva0yTbbWnFhTVO3cvA1cDX6tWyn4MHCpp9W60vxDbbwH/TQq2Jth+oEnZS2232m5992pdGvALIYQQeo0IasKyaijpApj8u7xovRP4VE67vqsN5OlBL9j+C2nUZaCkNSrFzgZOps7fou0ZwJ9Joz0LZNVprl5aQ7ZvBV4iffPfzG/z7wnAaNuv2X4OeCuPdlX1A+6WNAE4AdiyyBth+w3bz5OCn+1z+mjbM23PJ52LjzfpT7Pzhu2xwCY5/dZ29q0hSR/M+zLa9iRgeeW1V4ULga/UOw62XwKuZeFRsJ44d2OAybR/7kIIIYRlRgQ1YZmT10rsBlyWF+efTBpREYDtN0nf8J8I3NSNpoYCm+U2ngRWJ02depvt6aR1E/s32MbZwCks+Lc6ByiDozWB2V3o31v5BwBJ9+QF6ZcUZeYVZedV6q4g6Zxcpy2nXwxcYHtr4H+AVYo61Yt3t5O+AEkrkkaKTs/H9ELgM3XWKt1GWnfSnalnBwBrATNyW/1YcJoitl8kTU08qsE2/g84Eli1SFsk5y6EEEJY1kVQE5ZFXwSutr2B7Rbb6wMzgPWKMucDp9ie05UG8tqPIcA2uY0W0rSq6jQmgLNI08EWYnsKMIkFb14wEji4WHtyKHBfV/pZaWtQXpDe6CK9Xp1v5jq1NUjvBmblvlXXAw2WtHK+a9jOQC0Q2kFSvzzNbn/SlLx69gAesb1+Pqb9SKMx1bVKlwGn2p7c0f2oYygwqDh3H6H+uTufFLzVG2mbDdwCHFYkjyRNj1sxvz+MHjh3IYQQwrIugpqwLBpKutgs3QR8u/bG9kTbV3WjjV2AWbZnFWmjSIu81y0L2p4IjG2yrbNYMOC6FHgFGC9pPNAX+EE3+tqTTiMd2z8Cz1XyHgHuAB4iBR21/AdJwcEEYCrvTHmranTeFpieZ/tp2xd1sf9I2gh4P+8EXeR1R/MkbVdp6znSyFCjRSvnAesU5UcADwNjJI0jTcH7Vlf7GkIIIYREdqemc4cQQqdJOhOYbfvCSvog4Bjbg+vXDD2htbXVbW1t7RcMIYQQFjFJY4oZHj0mRmpCCCGEEEIIvVq952OEEDpB0jDS+pnSjbabPq1+WWL7Ow3S7wHuqabnGw9U/336Ur4TWY+RdARwTCV5lO1je7KdEEIIISxaMf0shBCWchu1vNvfP/Vji7sboYEvfvnOxd2FEEL4j4npZyGEEEIIIYRQRwQ1IYQQQgghhF4tgpoQQgghhBBCr7ZIgxpJ+0mypM3y+5b8/oyizNqS/iXpIknD8tPJx0maX7xuumhX0nhJ11XSrpQ0S9LKRTszK/34WlH+IkmH5dcjJbUWeS2SHs+vd5V0m6QvF/17U9KE/PpGSVMl9Snq3y5pgaeRL8kkbZ+Pz+5F2go57YoibSVJL0oaIemIBsej4WJ5SZ+W9IikKbnsdZLWy3mSdKqk6fl43itp86LuGpKuyflP5vO9es7bWNJcSY9KmizpYUmHFHXXlfS7/LmZJKnRc1HKbY3LZS/OfSvTJ+f2V8h1BkkaUWzjM5LG5PpTJH0/p5+ZP6Pjip/V2jk3Gys932SxkDQkfw42rvTJkk4t0t4n6d+SLpT03QZ/10c3aONMScfl19dI+quklfL790uaXpTdTNIdkqbl8zBc0jo5b5fi8zVF0lcqbVhSS5F2ck7rn98/U3yOx0m6oEF/Lyk+H3OL8vs1+xxLasvlnpb0QlFv/Zzf6O/wpc6csxBCCGFZsKhHaoaSng5eXtA/xYJPRx8CTASwfVZ+Onl/YG7tte0fNWogXyAsB+wi6V2V7PnA4Q2qPg98vXax1Fm2ryj6+izwyfx+CHAzMCz3bzCwou3hXWlnMamdt+oT1F8GBioHisCewNMAti8rjsfzwM75/bB6DUjaFrgQONj2ZsAA4Hpgg1zk66QHE25texPSwyVvLdq+Aphie2PbGwHPAD8rmnjC9gDbmwMHAd8oApszgdttb2t7C6Dunbkq2+oPbAv0Bz5bSd8a2BD4QpP9HJrb2gqYWRQ5r/ic97f9Sjt9Wdzq/U0DTAc+V7zfH3gcwPbp+Ti1Aq8U+3pxB9s0cGg1UemLg9uAH9v+cD7XPwfWkvQB4Brgq/nztTNwjKS9ik1MqOzH54HJlWZ2Lvp7fN3O2Ufl/fsc+TORf26hyefYdmuudzrwq6LeX/OmG/0dhhBCCKFikQU1kvoCOwFfYcELh7nAZL0zEnIAcEM3mvoS8Evgbha8qIJ0MXl87Rv0iheAe6lzsdQDTgeG5G98zwEafSP9jKSzJI3O3ygPlHS30sjDVxvU2Vdp5OHRXLb2rfSZkq6SdF/+1vrwnD4op41QMdLQqOOSliNdnB8K7F0J+gzcBeyd3w8FrqNrvgmcYfsJACcjbD+Q878BHG17bs6/g/RE+gMlbUoKDs4utncasFP5zfvbnbanAycCtRG/dUlBUC3/sY502Pa/gIeAjSvp/859+2Cdaqfk/ZxaK2v7px1pryZ/Y/+YpIeAo5qUOyp/jsYrjRj2yenXSPqppPvzaMHeOf0ISbdIukvSE5KaBndKI2EfBb7KwhfarwFP1kY5SEHNjZ3ZzyYuAE6StHwl/RDS7Zd/V0uwfa/tycDXgMtsj8vpL5A+c6cU9W8G9gOQtAkwG3ixh/pc0/Bz3KxSO3+HHSLpyDwa1Pbyq292vuchhBBCL7IoR2oGA3fmi7kXJQ0s8oaTLk7XI42mPNuNdg4gfcN/HQtfaD1N+qbzkGql7BzgxDoXS91i+3XgJGAUMNz2tCbFZ9reARgNXE66yNoROKNB+VHADrYHkC7KTizytiYFHDsBp0t6X07/KHBczt8c2LdJf3YhjYA8BTwA7FXJr527VfO2xjTZVjNbAmPrZUhakzS69ZdKVluutyXwqO23ahk5sBgPbNGgvbHAZvn1RcBVkv4g6duS1u1Ih/NI4G6kb/jL9D6kb+PvqlNtK5ofo5OLaUcLPa8luxL4b9sfA5p9Vm+0vb3tbYEngcOKvPWBT5BGmS4tRrw+QrrAHgh8qQhK6vk8cJvtKcBrkrap5Nc+Gy3A68BzTbbVGTOAh0lfYJSaHdst6+TVPj81LwF/V5oeO5TU/6r71cFpsFUd+Bw3097fYbtsX5pHg1pX79ulAekQQgih11iUQU15kTCcBQOOO4FP5bTru9qApO2BF/JFw72kqVFrVIqdDZxMnX21PQP4MwtfLNV7eE+nHuhj+1bSRdNP2ilaW88xARht+zXbzwFv5dGuqn7A3ZImACew4MXRCNtv2H6eFPxsn9NH255pez7pXHy8SX+anTdsjwU2yem3trNvHSJpnXzROE1pLYWof7xr6e3l122m9iJ/s78RKYjcAnhU0lpNurip0jqW+4FbbP++kj4HmG57YpNtNFJOPxu0UKeltYE+xQjWL5tsa5s8GlObVlV+Nm6w/VYeGfsr8OGcfpftf9h+DRhBNz4bwO9IF98H0o2/6wbOJo2ydPTfrHqfhXpp15P6+zngN3W2U04/azgNthN9aJZeau9YhxBCCKGwSIKafIG4G3CZ0uL8k0kjKgKw/SbpW9QTgZu60dRQYLPcxpPA6lTWNeSpR+NI02HqqXexNAcog6M1SVNTOuut/AOApHvyxfslRZl5Rdl5lborSDon12nL6RcDF9jeGvgfYJWiTvVCye2kL0DSiqSRotPzMb0Q+EydtUq3AefS9alnkNZRDQSw/XxeW3A50Nf2HGC+pH6VOgOBSbW6eYpOre/Lk0aiqmsiagaUebbn2P6V7YNJn49mF/O1dRIDbZ9RTSdNR/uEpE832M/tmmx7IZKuzue8FvA2Ol/VcleTRnS2Jq0b6snPxntJIz1X5s/GCaRRmTJYfAN4jLSO5OYmu9hpeXRoEmm0qKbZsZ1IWsNTqn1+Sr8hTZGdbvvVHujq2zrwOa6rE3+HIYQQQsgW1UjNF4GrbW9gu8X2+qQpJOsVZc4HTsn/8XdavqAdAmyT22ghTauq943mWaTpYAspLpbKmxeMBA4uLtgOBe7rSj8rbQ3KF8cN10XUqfPNXKd2gfZuYFbuW3U90GBJK+dv93cmTXMB2EFSv3zhvz9pSl49ewCP2F4/H9N+pNGY6lqly4BT89qFrjoX+G5eH1OzavH6PODHklYBkLQnaRrd9cU5+2ZR/lTyiFS1IUkfqm0vv9+9WG+yOmmR/9Nd3RHbzwLfyj9V5wLfUb5bmKTlJZ3Qzvb+K5/zz9meDbwhqfY4+IPqlctJ7yJNp1qRhUcfhyjZhDQVrTYlcg9J78nTCfclTXWqZ3/g8uJvej3StNEdKuXOA75he1Hcoess0hckNb8kBZNvT81SuqPeFqQphkfUpsjlv4nvkc7H2/II1Sk5b1Fo+DluUqejf4chhBBCyBZVUDMUuKWSdhPw7dob2xNtX9WNNnYBZtmeVaSNAraorpHI04Lqrt/IzmLBgOtS4BVgvKTxQF/SXYuWBKeRju0fWXjNwiPAHaTF7KfmaWwAD5KCyAnAVN6Z8lbV6LwtcIFs+2nbF3Wx/7VtPEr6tv/avEj9AdKIR23KzYWkEZTHJU0lBTCfy6MBkNaLbKV0U4UngRbgyKKJTZVupjAlb/N827WpW9sDYyU9Rjo2P8396Y5fA2sWwUe5nycBN0iaTDoH7y2KlGtq3r6db8WXgZ8p3Sig2WjCd0nTKX/PwiMB00l/H7cCR+bRUkgB7rXAo8B1tYX1dXT0szGhOM49yvZ40rqp2vvXSWuEjs9TFycBB5OmpD5DCvp/kT8DfwJ+lhfqV7d7bZP9LtfUXNGgTDPtfY7rae9Yr650k5HaT6fW+oQQQghLI9mdWioSllCSzgRm276wkj4IOMb24MXTs7C4SboG+LXtEZX0I4CtbB+3eHoW/lNaW1vd1tbWfsEQQghhEZM0ppiB1GMW9XNqQgghhBBCCGGR6hUjNZKGkdbPlG603fBp9aG5fOOB6vN7vmS74QLmLrZzBHBMJXmU7SVqyky+lfGVleTXbe+4GLqz2Cg99+juOlm79vQ6GUnfZcGF/5BugX5OT7bTk/JNPqrriP7P9tWLoz8dFSM1IYQQlhSLaqSmVwQ1IYQQuq5lw9V96mnVWCz0tC8fWu/7gBBCCKWYfhZCCCGEEEIIdURQE0IIIYQQQujVIqgJYTGR9Grl/WGSLireHylpSv75s6T/n73zDrerqPr/50sHkS4gJUapAuEN4VKkSRARBISIEK4BAaWooIKCqPBTpEnTgAZEwJdOEmqkg2AivdxAKi2USFNaXgyB0JLv74+ZQ+aenHNuhSQ36/M898nea+qe2Qdm7bVm1lZF2uQce6Vyv62kG4t6XsvHED8h6Yh29mespKFVsoskPZfreqRyZHU75WMlfaWNNkflI73HSrq3EreoSv5w3vNUKbO0UuDRZ/LfJZKWLtLXlnSzpKclPS7pSkkr5TH6b9UR2tvnMsdImihpXJZvluW75KPBx0p6TNIhDZ5lndzvMbnd8yR9rWhrWn6mMZIuKcqdJekltQ4mW3MOcz8r9c0orueqPWpBEARB8ElTvVE8CIK5AEm7AIcAW9l+XVI/YISkTW3/px1VDLd9mKTlgSclXW37hQbtfZH0kWMbSZ/KQSkrHGX7akk7AH8BNmynvD8p5tNabfR1kO0WSQeTglV+o0p+QJZ/Ncv/Ckyw/Z3c99+SAsLuqRTk8ibgp7ZvyOn9mRUb6G7bZaBdskK2C9DP9ntZWVxEKYjpecCmtl+UtCgpHlI9/ggMtv23XG8f2+OB2/L9KOBI2x/t2M+KzADgBVLsrVFFfbXm8CRSXC0kTbPdlyAIgiAIwlITBHMpR5OUg9cBbD8CXAwc2pFKbL9BCrz52Tayfhu4lHTyWb3I9XeRAsgrMAQAACAASURBVKS2V34/sGr7etq+eiStCWwMnFCkHw80SVqD9Bz3VxQaANsjbU9o0O5nSTGe3sv5X7f9MvBp0oefN7L8PdtPtlHPi0W74xvkrdAfmAD8mRR0czY6MIdBEARBMN8SSk0QzDkWL12hSIvzCusDo6vyt2R5u5HUC1gMGNdG1oHAcGAodRbXwK5ArYV6PfmOwIga8nq0p571gDG2Z1QS8/UY0thswOzjVrJ1lfvZGiRFbnVJT0k6R9KXc71TgOuBf0kaKmlQ6SJWg8HAPyTdIukIScu045mbSWN+HbBLtg61ogNzWF3uYEktklqmvfVBR4oGQRAEwTxHKDVBMOeYbrtv5Q/4dRv5BVTOYK91FnspGyhpIvAscJbtd+tWKm0CvGb7X8CdQD9JyxZZTs9K18HA99opfxa4DDi5jWcCuDzXsyVwZJX8RZLV6k+V7lL72evJq7m7HHPbz9ieRrL+HAy8BgyXtD+A7QOBrwAP5b79b72KbV8IfBG4CtgWeCC7rNVE0iLA14ERtqcCDwI7FFnaPYd1+nOe7SbbTUt+ejZdKQiCIAh6FKHUBMHcyWOkhXZJvyyH5BJVKh7LAa8X98Ntrw9sDfxe0soN2moG1pU0GXgGWArYo0g/KisAX61y46orJ7mRHUtymWuLQbme3av2/QwCPg9cAZydZROBjao21S8A/A/weE6vHrc2sT3D9ijbvyEFi92jSBtvezBpT88e9erIeV+2/b+2dwM+JFmO6rEjsDQwPo/9VrS2knVkDoMgCIJgviaUmiCYOzkNODVvEief/rU/cE5OHwXsm9MWBPYBRlZXYvt+0l6Zn9RqJCsEewIb2u5tuzewG/Vd0NqF7ZnAWcACkr7WhXo+IClHm0v6ou2ngUezrMKxwCM57QpgC0k7VxIl7SipT7028qll5WEGfUkuZ0tK2rZa3qCeHSvuY1kBWR54qcHjNQMHFuP+eWAHSUuUmdqawyAIgiAIQqkJgrkS29eTXJ3uk/QEcD6wj+1/5ywnAGtKGkta5D9NcveqxanAAZI+XSNtG+Al2+Xi+y5gPUld2phu28CJwM+7WM904PfMck37HrC20pHNzwBrZ1kl7y7AjyRNkvQYSRl8NZet3lPzLWBJ4GKlI5vHkfbtHEdyaft55Rhm4Le5rnrsAEzIc3IbyZJV86S6rLh8jXRSW+U53wbuIe0tqqbRHAZBEATBfI/SuiMIgiDoqfT+/FL+zXGbz+lu9HgO2O/2Od2FIAiCuR5Jo203dXe9EacmCIKgh7PC8mvHgjsIgiDo0YRSEwTzCZKOIe2fKbkqB3T8uNu+jrRnpORo27d93G13N3NyHIMgCIIgqE24nwVBEPRwmpqa3NLSMqe7EQRBEAThfhYEQRB0jlenTOKPl3f6ELqgHfx40DxndAyCIOhRxOlnQRAEQRAEQRDM04RSEwRBEARBEATBPM18o9RIGiDJktbN973z/QlFnhUkfSBpiKRjilgWM4rrH7fRzlhJQ6tkF0l6SdKiRTuTq/rxoyL/EEn75+tRkpqKtN6SJuTrbSXdKOmAon/vSxqfr6+S9JSkxYvyN0nau/Mj+ckiaZM8Pl8pZAtl2YWFbBFJUySNkHRgnfGou5Fb0s6SRudYJU9IOjXLT8xzNyan7VWUuUzSc0Vbd2f5gZJek/RojpVyq6TNq8rtLun6XO5pSf8t6tmsjTG5TNLunRvRriNpgqRLa/RpmqRPFbKz8zytWDzbf4rxHKMUOLS6/oUkvZmv18x1/KBIP1fSPvlakiqxZCbkOgfltEUl/UnSM3keRkhapWij7juU7yvzWMa1WadGf/sW6VOKd+K2nN4n/46fzP34VVF/w/c0/17vrmrvREmHd2TOgiAIgqCnMz/tqWkmBbbbmxRYD+BZUqC+/5fv9wQmAuSTjE4CkDTNdt+2GpD0RZKiuI2kT+VgehVmAN8F/lyj6KvATyT9xfb7HXwubF8IXJj7MBnob/v1fH8KcAxwbF4IL2x7WEfbmINU5q0ZuLOQTwX6SVrU9nukQIbPA9i+ALgAQNKLwNa236zXgKT/Ac4Edrb9lKSFgIOKLKfbPlNJIX5Q0jW2Z+S0I2yPqFHt5bYPz/VvD/xN0ta2n6pksP2NIv0w23NMUWkvkjYEPgS2k7R4DnZZ4VlS4MhhWVnZGvgP8H7l9yPpROB122d2oNlXgCMknW/7w6q0Q4H+QJPttyQtA3wjp50KLAqsbXuGpIOAa4Av5fS671DBR/NYD9tjgMrzXQZcXXknlIJs/g04yPadWem7TtIbtv9Cg/dU0vJAH+BdSb1sV/ctCIIgCILMfGGpkbQksCUp6nhppZgOPK5ZlpCBwJVdaOrbwKXA7cxaWFU4k7Qwq6VIvkZasO/XhbbrcTywp6S+wCmkReBsSHpR0kmSHpD0sKR+km7PX7kPqlNmN0kPKlkkbpe0YpafKOliSSPzl+nvZvn2WTZCyepxtiTV67ikBYA9SOOyk6RFimSTorbvlO+bgaF0jqOBEyoKh+0Pbc+mfNp+AvgAWLojldu+A/grrRWldiNpAUnn5DG7AVihTr6lJP1D0iOSxknaJcvXlDRR0qXZGnClsvUuz/spkh7Kc/mFNrrTDFwC/IP0QaBkKOk3BPAV4J8kZb6r/Ae4G9i3RtqvgO/bfgvA9pu2L5H0aWAf4KcVBdT2+QCSvpzLduc7VI99gVG278x9eBv4EfCLdpT9FjACGM6scQ2CIAiCoAbzhVID7A7cmhetUyT1K9KGAXtLWo20AHu5C+0MJC1AhpIWSCXPkywOtRZmkBSOn6mGO05XsP0OcCRwFzDM9qQG2Sfb3hx4gLQIHwBsAZxQJ/9dwOa2NwKuBX5WpPUhLRa3BI6XtFKWbwYcntO/COzWoD/bAE/Yfha4F9ixKr0yd0vkukY3qKsRG7SnrKRNgAm2pxTiwYUL0SUNij8CrNvJ/n2LFONlA+AHpDmpxXRgN9v9gO2BwUXaesDZtvsA7wKHFGn/Z3tT4C/AH9roy17Uf8cfB1aVtHRO606L4O+Ao7KiC4CkZUmWx3/VyL8W8JztaVXyFmD94r6td2hQlfvZInSM9avrtP0ksHxusxEVJavWWLeJpIMltUhqmTa1wwbgIAiCIJinmF+UmnKBNYzWC4Rbga9m2fDONpAXvK/lBdadJLeWZauynQwcRY1xt/0c8BDJ2tMqqUZzHQouZPsG4E3gnDayXp//HQ88YPtt268AM7O1q5pewO2SxgM/pfVicYTtd22/SlJ+NsnyB2xPzl/PhwFbNehPo3nD9iPA2ll+QxvP1hWOkvQUcB+zXBcrHGG7b/77ToM66lqk2sE2wFDbM22/CIxq0MapksaRrIWrS6pYdZ6z/UC+vozW416xTlxOfYUJSV8CXrT9EvB3YLOswJSMIFlD+5HGq1uw/TQwhtYWi0ZjKmr/TlrJ2/EOXV7Mb99OuIfW60fjQtKqpN/XA7YfAxbM7o/txvZ5tptsNy25VEd1sSAIgiCYt+jxSk32S98OuCDvNzmKtDASQF6kjCZZGa7pQlPNwLq5jWeApUiuUx9RLMz2mq104mSSK1Q5L28ApXK0HPB6J/o3M/8BIOmO/OX53CLPe0Xe96rKLpTdlMZIqkTxOxsYnL/+/xBYrChTvZBzG/JWSFqYZCk6Po/pmcDOKjaiZ24ETqNrbkMTgY0bpJ9ue21gEHCJ8oEPHWQjkiWjTSRtUVgGvp7Fs41TjXzfIbnG9ct7WF5n1pw0Gvf2LrqbgQ3yfEwiveMDqvIMI73Ht7r7I/ueRHLbqvx2pwAfSOpVI+9TwBdqKOP9gMeqZN3xDtVjItAqwJiktYE3shW1HgOB5YHn8nj3orXrbBAEQRAEBT1eqSG57lxi+3O2e9teHXgOWK3I83vgaNtvdKaB7BKzJ7BhbqM3ya2qlsvISSR3sNnIezYeo/VehVHAPsXek/2AkZ3pZ1Vb2+cvz9/vQJlf5DKVRdrSwEu5b9X7gXZXOn1qBdKG8YoitLmkXtnNbi+SS14tdgAetr16HtNepC/p1XuVLgB+Y7tdCkMdTiMdpLAmgKQFJf20OpPtK0lWrH06Urmk/qRDIv7anvy27yssAzeTLF175701qwJfrpNvaeBV2x9K+iqwalHt57M1EWYdvlBhYCG/t84zLEhS0tcr3vFvMrv17FngWODc2SrpIrYnkj4Y7FSITwHOyXtokLSMpIPyHpsrgNMrLmt5b9eCtv9ZVXV3vEP1uBTon9+BysEBfyS9c41oBrYvxnpTOuGCFgRBEATzC/ODUtMMXFclu4a0wRhIiyXbF3ehjW2Al7JbToW7gPUkfbbMmBdmjzSo6yRaK1znAW8BYyWNBZYEzuhCX7uT40hj+0/SCVUlDwO3APeTFoyV9PtISuR40tf066lNvXlr5Z5n+3nbQzrZ/0odj5IUzSslPZ779pk62Y8n7X2qKJnlnpryiOLKXoynSNa33fNeis5wNWlP1gRgCOndqsWlwBbZkrYnyZpSYSJwUHZN+xTpvaqwhKSHSPt1yn1RJf1JLmzlPI8E+hb7pQCw/efsTvlxcCKwenH/J5IiNlrpqPORQGUfzc9JVsZJkp4m7a37ZnWFbbxD1XtqGh63XaPut3O7x0l6EhhHUijrKn2S1gBWZtaHAPJeuPckVSyKx+VDHl7MlpwgCIIgmK9R93uIBPM7qnNsr+aho4t7EtkCdbVrHEuudJTwBm5w5HUw79PrC0v7yBM2bztj0Gl+POi2Od2FIAiCeQJJowuvn25jfopTEwRBMF+y4nJrxaI7CIIg6NGEUtNBJB1Dcu0pucopWGcA2D62jvwO4I5qeXaXqn4Xv51Pfeo2JB0IHFYlvsv2j7uznbmNfEBFzeCxtlerlkn6NbO7aQ2zfUp39ksprtHtNZK2nVstR0rxni6qEr9ju+6pcUEQBEEQfPyE+1kQBEEPp6mpyS0tLW1nDIIgCIKPmXA/C4IgCDrFy/83ieOu/Nqc7kaP5Li9wq0vCIJgbmB+OP0sCIIgCIIgCIIeTCg1QRAEQRAEQRDM0/QYpUbSAEmWtG6+753vTyjyrCDpA0lDJB1TxJ6YUVw33DQuaaykoVWyiyS9VIk0n9uZXNWPHxX5h0jaP1+PktRUpPXO8TaQtK2kGyUdUPTvfUnj8/VVkp6StHhR/iZJ80zkcUmb5PH5SiFbKMsuLGSLSJoiaYSkA+uMR8PDGvLY3F0lO1HStBwktCKbVtWPU4u0X0g6Nl9fJmn3Im0hSW/m6zVzn75e9HWapCfz9YWSnsub5Svlz5NUMzBrkWfdfLDCHEFScx6T3lV9cj5EoyJbJf+uzpB0fH7msZI+LMajZuBXSadIOixfD5M0WdLC+X41SU8UedeTdJukSZKekHRFZS4l9ZfUkuWPV35zRRszJa1eyH6Zn2ODfP8fSeOK/p5ep78X5PTHJb1T5P+GUsDU30p6Ov9W75C0Ti5XyfeCpFeL+1Vy+pdyf75ctLWYpNc7MGVBEARBMF/QY5QaZkVJLxf0zwK7FPd7koIQYvukSjR2YHoRmf2P9RqQ9EXSmG0j6VNVyTNIUeNr8SrwE0mLdOiJMrYvLPr6MtA/3+8JXAsck/u3O7Cw7WGdaWcOUZm36mjpU4F+yooi8DVSAEpsX1CMx6vA1vn+GOogaXmgD7CSpF5VyVOAI+oUnQ7sJWm5jjxUBds3F30dAwzM9weQgpCelvu3CSlq/Jn1a5srqPU7gxTos4w/NJAUxBTbv87PvxnwZvFbqxuAsooFgH2qhfk3eCPwB9tr2V4XuBBYPisrFwPfzfJtgSOUYiVVmJD7WWEA8ASt2aLo71G1Omf7wPx8A4DHivzXAz8lnTy3ge21SfN7g6RFivfiJOCSotzLuep6v40gCIIgCKroEUqNpCWBLYHv0XqxNR14XLMsIQOBK7vQ1LdJUdtvB75RlXYmadFU6/CF14A7gf260HY9jgf2VDpq9hTg0FqZlCKPnyTpAUkPS+on6XZJz0g6qE6Z3SQ9KOnRnHfFLD9R0sWSRuYv5N/N8u2zbISkxySdLUn1Oi5pAWAP0rjsVKX0GbgN2CnfNwND6TzfAkYAw2m9kAW4gBQ5fpka5d4H/hf4SRfarsc5wHr5S/wQ4FDbH1ZnkrS5kjXqPuDAepVJOjTP7ThJwyUtluXDJJ0j6R4lS9EOWf59SdfkuX1K0i8bdVbSskA/4BBqK6EvVKwcpPG+ul2j0DZ/AI7K70vJfsAdtj/aqW3777afBH4MnGt7XJa/AvwKOLoofzX56GpJ6wEvAf/tpj5XOIo0r+/mftwIjGX2Y+FbIWlBkpK4H7BLxVIVBEEQBEFteoRSQ/qf/622nwKmSOpXpA0D9pa0Gsma8nKtCtrJQNKieCizL+qeJ31V3bdO2VOAn+XFSrdh+x3gSOAuUiyRSQ2yT7a9OfAA8FfSl+UtgBPq5L8L2Nz2RiSL0M+KtD4khWNL4HhJK2X5ZsDhOf2LwG4N+rMN8ITtZ4F7gR2r0itzt0Sua3SDutqiohTVmrupwCXAj6oLZf4E7CdpqS60Pxu2ZwI/IClb423fWyfrRcCBORbKonXyQJr/TWxvCLxA63dxVWBr0pxfUCySNwH2Iikr+0vq06D+PYC/5fhBM7Ii0Kp90nytQVIOustN6mmylatKvgH134n1a6S1ZHmFKcD/SVqT9E7UsnDeV7iF1fxgUI/8EWCm7Rfb6Ect+pPeiWeBh4CvdqTt3P7B2f2u5Z2p73e0eBAEQRDMU/QUpaZckAyj9aL1VtKCoJmkkHSK7B70mu1/kawu/fKX65KTSV9mZxtX28+RFiffrk6q0VyHggfZvgF4k/TlvxHX53/HAw/Yfjt/wZ6ZrV3V9AJulzSe5EZTLsRG2H7X9qsk5WeTLH/A9mTbM0hzsVWD/jSaN2w/Aqyd5Te08Wx1kbRqfpYH8oJ8QeW9VwVnAt+rNQ45EOQVzG4F6465Gw08Tp25k7QysIDtB7Po0gbV9c3WmPEkS0A5X8OdeAz4D7BGlt9q+03b04C/kZTUejScL9Ic7Ux9BaErnAT8Aqhr+atCzD4XtWTDSdbdXaj9jpXuZ2d3oL/12mskL2lrrNvE9nm2m2w3LbFUpzxfgyAIgmCeYZ5XavJeie1IX58nk5SKgeTFj+33SV9sfwZc04WmmoF1cxvPAEuRvlx/RI7cPob05bsWJ5PcX8pxfwMolaPl6NwX7pn5D4C8IXmMpHLfwntF3veqyi6ktHl6jGZtRD8bGGy7D/BDYLGiTPWizG3IW5EtBQNIVp7JJKVi5xp7lW4k7TvpiuvZQGB54LncVi+q9oTYnkJyTay5eZ3kAnUwsEQh+7jm7vI8D9dVulerUI18lwAH5fn6Hd07XyuTFJ5L8xj+mNnHcDpJQatYn7oN2xNJv7vS8jcR2LhOkYlAdWCvfsBjVbIRpP4+lq2e3Ub+YLBQVqrb6sdHZDfM3YGT8lifAeyq4kCQIAiCIAhaM88rNSTf/Utsf852b9urA88BqxV5fg8cbfuNzjSQffn3BDbMbfQmLa5qfT09ieQONhu2nyAtZsrDC0YB+xR7T/YDRnamn1VtbZ+/LtdbpNcq84tcprIYXBp4Kfetej/Q7pIWVTppamuSSw3A5pJ6ZTe7vUguebXYAXjY9up5THuRvpRX71W6APiN7cfb+xw1aAa2L+ZuU2rP3e9JylstS9vrwHXA/oV4FMndquLKtT/dM3eD8jwMsP0fwNlSCDCoVr48R0sAr+RFcbVFcC8l1gVWIh2iAbCjpKWzMrkryQ2wFgOBv1TG0PZqJFfPTarynUr6rU3t7PM34ETSR4sKFwPbS/rINUvSrvkZ/wgcolknma1I+qhwWllh7ufR1fJu5AzgT5p1MuLXgY2AqxqU2Qm4x3av4rdxG63/uxEEQRAEQUFPUGqaSYvNkmtIm4KB9JXX9sVdaGMb4CXbLxWyu0ibvD9bZsxflB9pUNdJtFa4zgPeAsZKGgssSVoIzQ0cRxrbfwKvVKU9DNwC3E9SOirp95GUg/HAU8xyeaum3ry1Wozbft72kE72n7y/Y2VmKV3kfUfvSWr1lT8/w41APV+d04EVi/wjgAeB0ZLGkFzwGm627yT7A/8r6X7qbGS3bdJ8tZAWwBOqsjwL3E1yMTvI9gdZfg/JvelR0seB6nIV2jtfY21f1vYjdZzsjjixuJ9GUoJ/ng+seIxkPXrN9vOk0wgvVjoC+i7gT7b/XqPey2yPrdNsuafmgk50+wzSh4yJkp4iWYy/kS3I9WhrrJdVOvij8vfDTvQrCIIgCHoUSmuhIGg/kk4EXrd9ZpV8e+Aw27vXLhnMCSQNAy7LJ2+V8u8Da9puGBsnmPdZZY2lffDvNp/T3eiRHLfXbW1nCoIgCD5C0ujCK6jbqHX8cBAEQdCDWGXZtWLxHQRBEPRowlJThVJU9OoYElfZbhitPqhPPnigWoH+dj6JqzvbORA4rEp8l+0fd2c7PRmlaPY310ja2vZb3dzW8cy+h+py26d3ZzvdSXZBq/66dLrty+dEf9pLU1OTW1pa2s4YBEEQBB8zH5elJpSaIAiCHk4oNUEQBMHcQrifBUEQBJ1i8puTOOC66ti2QSMuHHDrnO5CEARB0AF6wulnQRAEQRAEQRDMx4RSEwRBEARBEATBPE0oNUHQA5E0QJJzIEok9c73JxR5VpD0gaQhko4p4rHMKK7rHrIg6TuSJkiaKOkxSUdm+UWSnsvlx0r6SlFmlKQni/qvzvLjJL2UZZMkXStpvapyTZIezHmel/RaUU/vNsZjlKTZ/Hcl9c0BMdsaz20lbdGOfPtLandcJUnT2kjvLalm7CBJh0taor1tBUEQBEFPJpSaIOiZNJMCa+5dyJ6ldVT6PcnBLG2fZLuv7b7A9Mq17T/WqlzSTsDhwA621wf60Tow6FG5rsOBc6uKDyrq/1YhH5xlawHDgX9I+kxZ0PZmud5fA8OLeia3Y0xq0RdoU6kBtgXaVGo+YQ4HQqkJgiAIAkKpCYIeh6QlgS2B79FaqZkOPF5YLAYCV3aymV8CR9p+GcD2u7bPr5HvfmDVjlZuezhwO/DtznRO0uKShkkaJ2k4sHiNPIsAxwMDs7VnoKTlJI3I5R6QtGG2An0fOCLn21rSrtlq9KikOySt1M5+fV7S/ZIeLq1mOe2oLB8n6bdF0kKSLs7yqyUtkS1oqwAjJY3szBgFQRAEQU8ilJog6HnsDtxq+ylgiqR+RdowYG9JqwEzgJc72cYGwOh25NsRGFElu7xwG2sUk+YRYN1O9u8HwDu2NwROAjauzmD7fVpbfIYDvwUezeV+BVySrUDnMsuSdDfJCra57Y1IY/rzdvbrLODPtjcB/lMRStoBWAvYlGQ92ljSNjl5HeC83KepwA+zBe1loL/t/rUaknSwpBZJLe9Ofb+d3QuCIAiCeZNQaoKg59FMWmiT/20u0m4Fvpplwz/GPpwu6VngMuDkqrTS/eyoBnWoC+1vk9vG9jhgXDvLbQVcmsv9A1he0tI18q0G3CZpPHAUsH47698SGJqvLy3kO+S/R5mlzK2V016wfW++viz3sU1sn2e7yXbTYkst0s7uBUEQBMG8SSg1QdCDkLQ8sB1wgaTJpAX3QLKCkK0To4GfAdd0oamJ1LB+FBwFrAkcC1zcyTY2Ah5vT8Z8MELF+lNxr5stsnCdfK2y1JDVilD8J2CI7T7AIcBi7elng/oE/K5Q9ta0/dc6+SNichAEQRBUEUpNEPQsvkVymfqc7d62VweeI1kWKvweONr2G11o53fAaZJWBpC0aPVJabZnktytFpD0tY5ULmkPkuViaFt5c1vXFQpBC3AXMCjXtQGwYZ18bwGfLqoqy20LvG57ao18SwMv5ev9OvBo9zJrn9OgQn4b8N28HwpJq0paMaf1kvSlfF05AIIafQqCIAiC+ZZQaoKgZ9EMXFclu4a0PwQA2xNtd9Z6UqnjZuBs4A5JE0nWn4Vq5DNwIq33nJR7au4o5JWN+JOAfYDtbL/WyS7+GVhS0rjc9kN18o0E1qscFAAcBzTlcqcwS2G5AahYebbO+a6SdDfwegf69RPgUEkPkxQjAGzfDlwB3J9d2q5mlsLyOLBf7tNy+dkAzgNuiYMCgiAIggCU1hxBEARBT2WFNZf2rqd/qe2MwUdcOODWOd2FIAiCHomk0bZruYB3idm+rAZBEAQ9i97LrBWL9CAIgqBHE0pNEAR1kXQMKUhnyVW2T5oT/ZlXiHELgiAIgk+WcD8LgiDo4TQ1NbmlpWVOdyMIgiAIwv0sCIIg6ByT3nyenf526JzuxjzFLbudPae7EARBEHSAOP0sCIIgCIIgCIJ5mlBqgiAIgiAIgiCYpwmlJuixSJqR44pMlDRW0k8lLVCV5yxJL1XkktaX9JSkxYs8N0naW9JKkm7MdT0m6eYGbfeWNL2IxzJG0iKS9pc0JOc5Lrc9JtfXXJTfXNKDOe3xnPeAoq73JY3P16fU6cP+kl6T9KikSZJuk7RFkT5KUlNx31vShHy9raT/VvV/+47PwidP1bhOknStpPWq8nxG0geSDilkV0j6QXG/maRxkhaS9N083uMkTZC0W4P2L5L0XG7/CUm/KdI+GnNJk4s6/ynpc5KWL8b7P8VzVN6faVVtffQ+BUEQBMH8TOypCXoy0233BcjR2a8gBTz8TZYtAAwAXgC2AUbZnijpWuAY4FhJuwML2x4m6S/A322flctv2Eb7z1TaryCpOs9g22dIWgsYLelq2x8AFwN72R4raUFgHduPARfmeiYD/W23FfhxuO3Dcpn+wLWS+tt+vI1yAHfb3qUd+eZGBts+A0ApqOY/JPUpgnnuCTxAClb6lyw7ghT88mrgDWAI8ENgZdL70M/2fyUtCXymjfaPsn21pMWAxyRdYvu5Gvn6235d0m+BY20fBFTe2eOAaZXnyLIO1OaiggAAIABJREFUDkMQBEEQzB+EpSaYL7D9KnAwcJhmrQz7AxNIEdqbi+zHA3tK6kuKKl/ZYf1Z4MWiznHd2L9JwDvAslm0IvDvnDYjKzRdbWMkKQr9wV2tq0K2ilws6fZsefimpNOyBeJWSQvXKLOkpDslPZLz7ZblvbNl4+Jsvbha0hI5bbKkUyU9lP/W7MBzDwduB75diJuBnwGrSVo153sFOAM4Dfg+MM72PaS5eAuYlvNNq6Og1GKx/O/bbeS7H1i1nXUGQRAEQVBFKDXBfIPtZ0nv/IpZ1AwMBa4DdqkswG2/AxwJ3AUMywoHwNnAXyWNlHSMpFXaaHKNwnWo4VFKkvoBk7LyBTAYeFLSdZIOyV/8u4NHgHXbmXfrKvezNerkWwPYGdgNuAwYabsPMD3Lq3kXGGC7H0mx/H2haK4DnGd7Q2AqyVJSYartTUkWlDPb+QwVPnpuSasDK9t+CLgSGFjkOxdYDzgK+HmWjQVeAZ6TdKGkXdvR3umSxpCU4GHFvNZjR2BEO+pdvJwTkgJeE0kHS2qR1PL+1OntqDoIgiAI5l1CqQnmNwQgaRHg68AI21OBB4EdKpls3wC8CZxTyG4DvgCcT1ogPyqpkRvSM7b75r965+keIenJ3P5xRVvHA03MsjB0Vzj40n+pVpCqUnZ30f++tp+pU+ct2WVuPLBg0dfxQO86fThZ0jjgDpKFYqWc9oLte/P1ZcBWRbmhxb9fqtOXepTPvTdJmQEYRmGlsz2T5I52i+03smwGSen4FvAUMDi7hjXiqOx6uDLwFRV7maoYKelVYHuSe2RbTC/nBPh1vYy2z7PdZLtpkaUWr5ctCIIgCHoEodQE8w2SvgDMAF4lLVKXBsbn/Slb0doFDWBm/vsI21NsX2F7X+Bh0l6crjDY9joka8ElpUXG9jO2/wx8BfgfSct3sS2AjYDKfpo3mOXuBrAc0HCPjqRDC0tBxVL1Xu7vTOADz4roOxNYKG+4r5T5BjCItCdl47wwf4VZblrVipbbcd0eyuduBvbP8349aWzXKvLWmnfbfsj270hK0R7tadT2NGAUrZWzkv7A54CJNLC6BEEQBEHQmFBqgvmCbFE5FxiSF93NwIG2e9vuDXwe2KGyh6NOHdsVezw+TXK7er47+mf7WqAF2C/Xv3PhkrUWSRl7syttSPoyaT/N+Vk0CtinaGc/YGQb/Ty7sBS83J52bT9YlLmepEy+avuDfHjB54rsvSRVrDDNwD1F2sDi3/vb0zaApD1IVrihktYBPmV71WLuK4pKvfKrZPfACn2Bf7Wz7YWAzYB6Vi5sTwcOB74jabn21BsEQRAEQWtCqQl6MpX9BxNJbk63A7/NisnXgJsqGW2/TVpAN9ovsTHQkt2m7gcusP1wN/b3eKBy7PS+pD01Y4BLgUHZDaqjDMxj8BTwK2CP4uSz80gb4MdKGgssSdooX6F6T823OvtgVVwONElqIVltnijSHgf2y2O8HOkQhwqLSnoQ+AnppLJGHJH7PAnYB9gun3zWTNpDVXINs1vpShYGzsiHGIwhKVU/aaP9yp6acSQ3vGsbZbb9b5JbXT03xSAIgiAIGqBZniJBEARzDkm9gRttb1AjbTLQ1I4jrIMaLL3mit7i93vO6W7MU9yyW8OzPYIgCIJOImm07aa2c3aMiFMTBEHQw1lrmV6xSA+CIAh6NKHUBEEXkNSH5B5W8p7tzT7BPhzA7O5Q9zY4cW2uxPZkYDYrTU7rXS2TdAwpiGbJVbZP6vbO1SAf071llfgs2xd+Eu0HQRAEQTCLcD8LgiDo4TQ1NbmlpWVOdyMIgiAIwv0sCIIg6ByT3vw3X7/uxDndjXmGmwccO6e7EARBEHSQOP0sCIIgCIIgCIJ5mlBqgiAIgiAIgiCYpwmlJphjSJpRiSMjaaykSoyWMs9Zkl6qyCWtL+kpSYsXeW6StLeklSTdmOt6TNLNDdruLWl6VRyWRSTtL2lIznNcbntMrq+5KL+5pAdz2uM57wFFXe9LGp+vT6nTh/0lvSbpUUmTJN0maYsifZSkpuK+t6QJ+XpbSf+t6v/2HZ+FT56qcZ0k6VpJ61Xl+YykDyQdUsiukPSD4n4zSeMkLSTpu3m8x0maIGm3Bu1fJOm5Ytzuy/L9JVnSV4q8A7LsW/l+lKQn8zt2r1Iwz9nmqii/laSHcoybJyQdnOU7SLpfSoFPJS2Y+7JF1fhU/pYp5vzR3Ie7JO3S2XkIgiAIgp5E7KkJ5iTTbfcFkLQicAUp2vxvsmwBYADwArANMMr2REnXAscAx0raHVjY9jBJfwH+bvusXH7DNtp/ptJ+hbzGLBls+wxJawGjJV1t+wPgYmAv22MlLQisY/sx4MJcz2Sgfzviqgy3fVgu0x+4VlL/IkBmI+62Pa8uagfbPgNA0kDgH5L65ACZkE41e4AUFPMvWXYEcL+kq4E3gCHAD4GVSe9DP9v/lbQk8Jk22j/K9tU15ONzm3fm+72BsVV5BtluyQrK6cA3ajUgaWXSO7277UckrQDcJukl2zdJ+i7wPeAC4EfAw7bvk7RDOT5FfVDMuaS+wAhJ023fSRAEQRDMx4SlJpgrsP0qcDBwmGZpFv2BCaSo8mXE9+OBPfOi7hRmRWH/LPBiUee4buzfJOAdYNksWhH4d06bkRWarrYxEjiPNA7dQv7qf7Gk2yVNlvRNSadlq8atkhauUWZJSXdKeiTn2y3Le2drw8XZInK1pCVy2mRJp2arxEOS1uzAcw8Hbge+XYibgZ8Bq0laNed7BTgDOA34PjDO9j2kuXgLmJbzTbP9XMdHC4C7gU0lLZyVozWBMXXy3pXT63EocJHtR3K/Xgd+Dvwipx8B/FLS+sBhwNEd6ajtMaTfwmEdKRcEQRAEPZFQaoK5BtvPkt7JFbOoGRgKXAfsUlmA234HOJK0qByWFQ6As4G/Shop6RhJq7TR5BqFe0/DyISS+gGTsvIFMBh4UtJ1kg6RtFgHH7cejwDrtjPv1lUuSmvUybcGsDOwG3AZMNJ2H2B6llfzLjDAdj+SYvn7QtFcBzjP9obAVJKlpMJU25uSLChntvMZKnz03JJWB1a2/RBwJTCwyHcusB5wFElBgGRJeQV4TtKFknZtR3unF+N2eSE3cAfwNdJ4Xd+gjl1Jlp16rA+MrpK1ZDm2/00ap/uBE21PKfIdUfRvZIM26r4vkg6W1CKp5f2pbzeoIgiCIAjmfUKpCeY2KnsMFgG+DoywPRV4ENihksn2DcCbwDmF7DbgC8D5pIXeo5IauSE9Y7tv/qsXqPIISU/m9o8r2joeaGKWheHWDj5nPUr/t1pBpErZ3UX/+9p+pk6dt2SXufHAgkVfxwO96/ThZEnjSAv8VYGVctoLtu/N15cBWxXlhhb/fqlOX+pRPvfeJGUGYBiFlc72TJI72i2238iyGcCOwLeAp4DBko5ro72jinEbVJU2LPdh7+KZSi6XNIYUePPINp6prTk8G1jQ9kVVeQYX/evfRhs1sX2e7SbbTYss9akGVQRBEATBvE8oNcFcg6QvADOAV0mL1KWB8Xl/yla0dkEDmJn/PsL2FNtX2N4XeJi0F6crDLa9DslacElpkbH9jO0/A18B/kfS8l1sC2AjoLKf5g1mubsBLAc03KMj6dDiC3/FUvVe7u9M4APPirg7E1hIacN9pcw3gEGkPSkb5z1HrwCV565epLsd1+2hfO5mYP8879eTxnatIm+tebfth2z/jqSM7NHB9su6HgI2AFaw/VSNLIOysrG77RcaVDWRpPiWbAx85KqY56QrEZDLcQuCIAiC+ZZQaoK5gmxRORcYkhfdzcCBtnvb7g18HtihsoejTh3bFXs8Pk1yu3q+O/pn+1qS69B+uf6dC5estUjK2JtdaUPSl0n7ac7PolHAPkU7+wGNXJGwfXbxhf/l9rRr+8GizPUkZfJV2x/kwws+V2TvJalihWkG7inSBhb/3t+etgEk7UGywg1VOk3sU7ZXLea+oqjUK79Kdg+s0Bf4V3vbr8MvgV91sY6zScpZ5TCM5YFTSXuCukw+COP/5XaCIAiCYL4mTj8L5iSLZzeehYEPgUuBP2TF5GvAR8f52n5b0j2kfQzD69S3MTBE0ockhf0C2w93Y3+PB66QdD6wL8nN6Z3c90HZDaqjDJS0FbAE8BywR3Hy2XkkN7qxkkxSqn5ZlN06j1+FE+uc6NVRLgdukNRC2iT/RJH2OLCf0klzk0iHOFRYVNKDpLGvtqpVc4SkfYBPkQ6D2M72a5IOJe2hKrmG5BJ2Qp26FgbOyJapd4HXSAcJNOJ0SWXY+E3LRNu3tFG+FjdJ+iBf3297z/yM52clW8CZ2XWyLSrjU2H3/O/Wkh4lvS+vAj+Ok8+CIAiCADTLEyUIgqA+knoDN9reoEbaZKCpHUdYB3OApddc1Vue/oO2MwYA3Dzg2LYzBUEQBJ1C0mjbs8V26yphqQmCIOjhrLXMZ2OhHgRBEPRoQqkJejSS+pDc2kres73ZJ9iHA4CfVInvbXDi2lyJ7cmkDfS10npXyyQdQwqiWXKV7ZO6vXM1yMd0b1klPsv2hZ9E+0EQBEEQfHKE+1kQBEEPp6mpyS0tLXO6G0EQBEEQ7mdBEARB55j05qvsfO0f53Q35glu+uaP53QXgiAIgk4QRzoHQRAEQRAEQTBPE0pNEARBEARBEATzNKHUBHMFkmbkiPYTJY2V9FNJC1TlOUvSSxW5pPUlPSVp8SLPTZL2lrSSpBtzXY9JurlB270lTc/tV/4WkbS/pCE5z3G57TG5vuai/OaSHsxpj+e8BxR1vS9pfL4+pU4f9pf0mqRHJU2SdJukLYr0UZKaivvekibk620l/beq/9u3Y8wnS1qhrXzdTX6WJyWNk/SEpCGSlqnKM0CSJa2b7xfLefsUeX4u6VxJC0j6o6QJeZwflvT5Bu1PLuZjjKQ/ZvlFkt7JMWUqec/K/Vgh31fe0wmSrtKsYK/T6rR1cO73E5IeyjGJkHSypFOLfJ+T9KykZYrxqfTv6pynfAcnSbpW0nodn4EgCIIg6HnEnppgbmG67Urk9RWBK0iR7X+TZQsAA4AXgG2AUbYnSroWOAY4VtLuwMK2hykFh/y77bNy+Q3baP+ZSvsVJFXnGWz7DElrAaMlXW37A+BiYC/bYyUtCKxj+zHgwlzPZKB/O2K4DLd9WC7TH7hWUv8iGGcj7ra9SzvyzS0Mst0iaRHgd8DfgC8X6c3APcDewHG235V0OHCOpG2AVUjBWZuAgfl+Q9szJa0GvN1G+/Xm42lgN+Cy/M71B14q0sv39HJSkM8/1GpA0i65j1vZfl1SP2CEpE1JgUQflXRRnt+zgP9n+8383g2yXWtn/2DbZ+T6BwL/kNTH9mttPG8QBEEQ9GjCUhPMddh+FTgYOEyzNIv+pMjzf6Z1tPrjgT0l9QVOASrHJH8WeLGoc1w39m8S8A6wbBatCPw7p83ICk1X2xgJnEcah25B0vKSbs/WoL+QItzXyreppPtyvvskrZPl+0v6m6RbsyWhonD2zpaIi7P15eqKBaMtbL8P/BzoJel/cn1Lko5i/h5JqankvZU0zt8BBpOUnf8jzfW/bc/M+V7M8s4wlKQkAWwL3At8WCfv3cCaDeo6GjiqojzZfoSkAB9qezrwU5KSthPwaduXd6SjtocDtwPf7ki5IAiCIOiJhFITzJXYfpb0fq6YRc2kBed1wC6SFs753gGOBO4ChmWFA+Bs4K+SRko6RtIqbTS5RuHuc3ajjPmL+6SsfEFaYD8p6TpJh0harIOPW49HgHXbmXfrKvezNWrk+Q1wj+2NgOuBXnXqegLYJuf7NXBykbYpMAjoS1ImKy5x6wDn2d4QmAr8sJ39xvYMYCyznnV34FbbTwFT8nhXOBw4CfiM7Ur8oSuBXfNz/17SRu1odmQxVkcU8knAZyQtS3rnhtUqLGkhYCdgfIM21gdGV8lashzbNwNTgEuYfbwuL/p3eoM26r4j2fWtRVLL+/+t6R0XBEEQBD2GcD8L5mYEkF2Uvg4cYfstSQ8COwA3Adi+QdKbwDmVgrZvk/QFYEfS4vNRSRs0cNOZzf2sBkdIOgio1Ftp6/jsirQD6at5M+krf1cpLSm1AkqVsva4n20DfBPA9k2S6lkzlgYuzm52BhYu0v5u+w2A7Pq3FTACeMH2vTnPZcCPgTPa6E9J+azNwJn5eli+fyT3+2VJ/wBurGS2/WK2Jm2X/+6UtKftOxu018gd8FqShWgzkvtYyeKSxuTru4G/tvlkrRGt5+1sYHHbT1blq+d+Vqu+mtg+j2TtY+k1e0VAsiAIgqBHE0pNMFeSFZIZwKvArqSF9vjsjbYEyf3rpqLIzPz3EbankPbmXCHpRtKi/poudKuyp+abwCWS1rD9bm7rGeDPks4HXpO0fGXx3wU2Air7ad5glrsbwHJAwz06kg4FDsq3X8//zra4rZHvBGCk7QGSegOjiuzV5d2GvE3yPqQ+wOOSlicpJhtIMrAgYEk/96xIwbXm+j3gFuAWSa+QrD2NlJpGDCMpURfnPTpl2vR2KL8VHgM2Bv5RyPpleYXZnqWDbESy/gRBEATBfE24nwVzHZI+A5wLDMkL2WbgQNu9bfcGPg/s0GjfhqTtNOtkqk8DawDPd0f/bF9LWkjul+vfudj7sxZJGXuzK21I+jJpP835WTQK2KdoZz9gZBv9PNt23/z3MslFb1CufyeyklQj39LM2hy/f1W1X5W0nNKJc7uT9pxA2hPzpXxd2eTfnudcmHRQwAt539O3gEtsfy7P9+rAcySLUL06+lXcC/Pm/g2Bf7Wn/VrYfp50+MQ5beVtg9OAU7OiRt73tX831Euubw+SdXBod9QXBEEQBPMyYakJ5hYqbj0LkzZmXwr8ISsmX6NwA7L9tqR7SBac4XXq2xgYIulDkvJ+ge2Hu7G/x5MsQOcD+wKDJb2T+z4o7xPpKAOVjvxdgrSQ36M4+ew80t6JsdmC0QL8sii7deEWBXCi7aur6v8tMFTSI8A/qa/knUZyP/spra0MkJSVS0kb5K/IJ5j1JlmU9ssHEEwiHejQiMslvQcsCtxBOnEMkkJUfez1NSS3vrvr1LUicL6kRfP9Q8CQNtofKakyR+Nsf6dMtP2XNspXs4SkF4v7P9j+g6RVgfvynL0F7GP73+2o73JJ0/P167YrR3QfIWkf4FOkgzO2i5PPgiAIggA0y6MjCIKgPpL2B5oqx04X8t7AjbY3mAPdCtrB0mv28lanHTmnuzFPcNM3fzynuxAEQdCjkTTadlPbOTtGWGqCIAh6OGsts2Is1oMgCIIeTZtKjaSVSEe6rmJ7J6UI1l+y3dFTf4JgjqIUjf7SKvF7tjf7BPtwAPCTKvG9tg+tlX9uwvZFwEU15JOB2aw0kq4j7X8qOdr2bR9D92Yjn5K3aJV4X9uNjmEOgiAIgmAepE33M0m3kCKjH2P7f3J8hkdt9/kkOhgEQRB0jaamJre0xCFpQRAEwZxnTrqfrWD7Skm/BLD9YbHBNgiCIJjLmfR/r7PzNRfM6W7MUW7a48A53YUgCILgY6Q9Rzq/nY8kNYCkzYH/fqy9CoIgCIIgCIIgaCftsdT8FLgeWEPSvcBnSLEkgiAIgiAIgiAI5jgNLTU5kN1iwJeBLUixQtbPQfKCoNNIGiDJktbN973z/QlFnhUkfSBpiKRjJI3JfzOK64ZHOkkaK2lolewiSS9V4prkdiZX9eNHRf4h+ThjJI2S1FSk9ZY0IV9vK+lGSQcU/Xtf0vh8fZWkp3Lgykr5myTt3fmR/GSRtEken68UsoWy7MJCtoikKZJGSDqwznicVKeNAyWdma9PlDRN0gpF+rTiehVJV0p6WtJjeTzXzGl98nw9KWmSpF9VtWGlIKcV2Z5Ztnu+vyeXrfS9ZkwkSb+u824emtN/IOmJ/PegpC2y/Pqc72lJ/y3KbZbTV5L0oaTvVbX3oqRl2jNfQRAEQTC/0FCpsT0T+L3tD21PtD3B9gefUN+Cnk0l6ny5oH8W2KW43xOYCGD7pErUe2B65dr2H+s1IOmLpHd8G0mfqkqeAXy3TtFXgZ9IWqRDT5SxfWHR15eB/vl+T+BaUrR68uJ5YdvDOtPOHKIyb81V8qlAP80KgPk1cnBP2xcU4/EqsHW+P6adbU4BjqgWShIwArjd9pq21wP+H7CSUtDWvwEn2F4H6AtsK+mQoorxVc+xNzC2qpmBxbs2sFbnbB+fn60JeKvIf3ae4wOALWyvCxwKDJP0GdvfyOW+D4wsyj1YaRu4n9nHOgiCIAiCKtqzp+Z2SXvkBUQQdBlJSwJbAt+jtVIzHXi8sIQMBK7sQlPfJh3hfDvwjaq0M0nR2Wu5YL4G3Ans14W263E8sKekvsAppEXubOSv8SdJekDSw5L6Sbpd0jOSDqpTZrdsCXg0510xy0+UdLGkkdli8d0s3z7LRmQrx9mNfudKlts9SOOyU5XSZ+A2YKd83wwMpXu4ABhUwzrxVWCa7Y92wNt+xPa9wL7AKNt3ZvnbwI+AXxTlRwFbZEvTUkAvYEI39bnC0cCRtqfkfrQAlwM/bEfZZuBw4AuSVu7mfgVBEARBj6I9Ss1PgauA9yRNlfSWpKkfc7+Cns3uwK22nwKmSOpXpA0D9pa0Gsma8nIX2hkIDCctrqu/dj9PsjjsW6fsKcDPJC3YhfZnw/Y7wJHAXcAw25MaZJ9se3PgAeCvwACSG+gJdfLfBWxueyOSRehnRVofksKxJXC8UvwpgM1IC+c+wBeB3Rr0ZxvgCdvPAvcCO1alV+ZuiVzX6AZ1dYSpwCUkpaRkgwZtrF+dZvtJYPncP4CZJMVme9LYjqhRz/DCLeyUTvR9vRp9bMn9q4uk3sCytkcDVwN7dbRhSQdLapHU8v7UtzpaPAiCIAjmKdpUamx/2vYCthexvVS+X+qT6FzQY2kmLYDJ/5YKx62kL/DNJIWkU0jaBHjN9r9IVpd+kpatynYycBQ1fge2nwMeIll7WiXVaK5xsKfZ674BeBM4p42s1+d/xwMP2H7b9ivAzGztqqYXybI6nvQxolw4j7D9ru1XScrPJln+gO3JtmeQ5mKrBv1pNG/YfgRYO8tvaOPZOsqZwPfqPHctRPvmZRjJWrg3s56tpHQ/+0WN9M7Qnr6V7/9sY90ebJ9nu8l20yJLfbqjxYMgCIJgnqLN088kbVNLbvuu7u9O0NNROh58O2ADSQYWJC3wzgGw/b6k0SQrw/rArp1sqhlYV/kAAGApkutU6ar0tKQx1P8KfjLpK3n5rr8BlMrRcsDrnejfzPwHgKQ7gBVISsb3s/i9Iu97VWUXypaDHYEPcxCrs4GTbd8saXtau1pVL6LdhrwVkhYmWTO+Luk3JEVwmbxXqezbjcBpJOVo1Vp1dQbbUyRdSdp/UmEirfdgUZW2aSmQtDbwhu13Ci+7+4FzSXthnvkYvGwfBzam9TvUD3isjXLNJKtSxQVyFUmfz8p2EARBEPx/9u483qqq/v/4650jzmY55IQiUk6h3rIcQU2zKDU1vGqJ38oGrb6UZoV9I8ucv6RfTEXLoVRMhTSH1BISh9SLXkYFJM3UVLRfGoqo+P79sdaGzeGcc88d8HLh83w87oN71l57rc/eZwNnnTWFCo0MPzu59PMj0jeww5diTGH5djhwle0tbfe2vTnwJLBZKc95wCm2X+5IBXnuxxHATrmO3qRhVdW+7T6dNBxsCbYfJ334LH9wHg8cU5p7ciwwriNxVtS1f+4N+FrbuRee8/18TjEHaV3g2Rxb5XygQyStprSK2F6kIVAAH5O0RR5m93nSkLxqDgAetr15vqdbkP4tqJyrdBnwY9uPNXod7XAeaS5K8e/WncA6xRwhAEm7SdqLNJdqoKSBOX0N4AJSg2sh2wZ+APyQpeNs4OyilzAPtTwGuKjWCZK2A1ayvWnp+T2HxeefhRBCCKGkkeFnnyn9fII0jv2FpR9aWE41A2Mr0m6k9KEyr7R3ZSfq2Bt41vazpbR7gO0kbVLOaHsa8Eidsk5n8QbXKOA/wCRJk4C1gHM7EWtXGk66t39hyb+jDwO3k3omfpyHsQHcT2osTAFmsmjIW6Va79tiw/NsP217ZAfjryvHfAuwan5tUmP1U3kBhanAqcBzeWGAQ4DhkmYAk0kNtourlHur7b/UqLY8p+aODsQ8htTA+qukx3P9R+VhgLUcRdv3elpeTOIZSWcTQgghrOCUPhe044T0LfBk2zsunZBCCF1J0s+Al2z/oiJ9f+BE24d0T2Th3bJun97e8+xTuzuMbnXrYV/u7hBCCCEAkiaWRpl0mUbm1Pwfi8bZv4e030PlXg4hhBCWUX3Xf198qA8hhLBca7NRw6Kx9wBvA9fmfSBC6HaShpHmz5Rdb7vqbvUrIttVv6K3/SfgT5XpklpY8t+Go2y3Nbm9XSR9GTixIvke29/qynq6kqT/AT5XkTzadkeWew4hhBBCF2lz+Jmkb9s+v620EEIIy6ampia3tLS0nTGEEEJYypbW8LNGGjWP2N6lIu3RvMFfCCGEZdx6fbb2nmfV2rN1+XfL4Ud3dwghhBCyd31OjaRm0mo7W0kqr4i0NmmvjhBCCCGEEELodvXm1NwP/JO0IeB5pfT/kJZHDSGEEEIIIYRuV3OfGtt/tz3e9sdt/6X084jtt9/NIEMIyxdJC/LeL9MkTZL0nbxpajnP+ZKeLdIlbS9ppqRepTy3SjpS0kaSbsllTZd0W526e0uaV9p/plXSqpKGSBqZ8wzPdbfm8ppL539M0oP52GM573Glst6UNCX/XnUBgVzXHEmPSpol6Q5Ju5eOj5fUVHrdO+/Dg6QBkl6piH//9r8LIYQQwvKjkSWdPwb8H/Ah0qZ3KwGv2V5nKccWQlh+zbPdH0DShsA1wLrAj3Pae4BDgX+QNlMdb3uapDHAMOBUSYcAq9iAF6BSAAAgAElEQVQeLekS4K5iARNJO7VR/+yi/kLagmsxI2yfK6kvMFHSDbbfAq4EPm97kqSVgH55ZbjLczlPAQNtv9RGDNfZPjGfMxAYI2mg7cfaOA9ggu1BDeQLIYQQVgg1e2pKRpJ2E58F9AK+TGrkhBBCp9l+ETgeOFGLWhYDganARaR/fwqnAUdI6g+cCZyQ0zcBnimV2WVDZG3PAl4H1s9JG5KG5mJ7QVcsdW17HDCKdB9CCCGE0E6NNGqw/QSwUv4P/HLSB44QQugStv9G+vdow5zUDFwLjAUGSVol53sdOAm4h7Q/zKyc/0LgV5LGSRom6QNtVNmnNHTrwnoZJe0CzMqNL4ARwAxJYyV9VdLq7bzcWh4BPthg3r0qhp/1qRL38ZJaJLW8+eqrXRRiCCGEsGxqZPPN1yWtCrRKOpv0DeWaSzesEMIKSAD535tPAUNt/0fSg8ABwK0Atv8g6d/AL4sTbd8haWvgk8BBwKOSdrA9p0ZdSww/q2KopK8ARblFXadJujrHdBSpATag3Ve7pPL4t2pr7ZfT2hx+ZnsUqfeH9fpsXX/t/hBCCKGHa6Sn5gs534nAa8DmwGFLM6gQwoolN0gWAC+SGhDrAlPy/JQ9WXwIGsA7+Wch2/+yfY3tLwAPk+bidMYI2/2AwcBV5R4Z27NtXwTsB3xY0gadrAtgZ6CYT/Myi4a7AbwXaGuOTgghhLDCarNRY/vvpG8QN7H9E9vfycPRQgih0yS9H7gYGOm0G3Az8GXbvW33BrYCDpC0Rp0y9i2OS1ob6AM83RXx2R4DtADH5vI/XZr705fUGPt3Z+qQtA9pPs2lOWk8cEypnmOBcZ2pI4QQQlietdmokfQZoBX4Y37dv2IzzhBCaK9exZLOwJ+AO4Gf5IbJgeShZgC2XwPuBT5Tp7xdgRZJk4EHgMtsP9yF8Z4GFMtOf4E0p6YV+A1wtO0FHShzcL4HM4EfAoeVVj4bRdoTbJKkScBawLmlcyvn1Bze0QsLIYQQlgdKX4zWySBNBPYlLam6c06bbLutJVNDCCEsA9brs7X3POun3R1Gt7nl8KO7O4QQQgiZpIm2m9rO2T6NLBTwtu1XquzhEEIIoQfYZv33xgf7EEIIy7VGGjVTJR0FrJQ3ofsWcP/SDSuEEDpH0o6k4WFl823v9i7GcBzw7Yrk+2yfUC1/CCGEEDqmZqNG0m/yKkKzge2B+aR9I+4AVtxxDCGEHsH2FKCtZZuXdgyXA5d3ZwwhhBDCiqBeT82ukrYkLWc6EDivdGwN4I2lGVgIIYSu8cT/+zefuWFMd4exVP3h8M91dwghhBC6Ub1GzcWkFc+2Ji1nWhBpE7itl2JcIYQQQgghhNCQmks6277A9oeAX9veuvSzle1o0IQQQgghhBCWCY1svvn1dyOQsIikBcUeHpImSSr2xyjnOV/Ss0W6pO0lzZTUq5TnVklHStpI0i25rOmSbqtTd29J8yr2wFhV0hBJI3Oe4bnu1lxec+n8j0l6MB97LOc9rlTWm5Km5N/PrBHDEElzJD0qaZakOyTtXjo+XlJT6XVvSVPz7wMkvVIR//7tfxfefRX3dZakMZK2q8jzfklvSfpqKe0aSV8vvd5N0mRJK0v6r3y/J0uaKungOvVfUWu/E0lDJb0had1S2hqSrs7lT5V0r6QtS/f9+dL1tEpatUbZxfM+VdL1WrSJ5tyKfAufwfz6eEmP55+HJO1ZOjZeUkvpdZOk8fn3dj0jkjaWNFrS7OLvj6Rty89dKe9wSSeVXq8s6SVJZ1Tkqxlffv3RnGeWpEeU/i7vWKrj2Yr416sVfwghhLAiaGT1s/Dum2e7P4CkDYFrgHWBH+e09wCHAv8A9ibtITRN0hhgGHCqpEOAVWyPlnQJcJft8/P5be0xNLuov6All/QeYftcpRXxJkq6wfZbwJXA521PkrQS0M/2dPJkaUlPAQNtv9RGDNfZPjGfMxAYI2lgaXPCeibYHtRAvmXRCNvnAkgaDNwtaUfbc/LxI4C/As3AJTltKPCApBuAl4GRwDeAjUnPwy55Wfa1gPd3MK5m4GHSc3dFTvs28ILt4sN2P+D50rM7HJhbXE8d5ef9auBrwP/WO0HSIOCrwJ62X5K0C/B7SR+1/XzOtqGkg2zfXqWIhp4RpQd/LHCl7SNzWn9gI9Lfv7YcAMwAPi/ph158Y7Cq8UnaCPgdcJTt+3PankAfYErONqKB+xpCCCGsMNrsqQndy/aLwPHAiVrUshgITAUuIn3YLJwGHJE/dJ0JFMvGbgI8UypzchfGNwt4HVg/J20I/DMfW5AbNJ2tYxxph/XjO1tWIX/bfaWkOyU9Jelzks7OvQ5/lLRKlXPWkvTn/M35lKLXI39j/3gub7KkG0q9DU9JOiv3JDwkaZt2XPd1wJ3AUaXkZuC7wGaSNs35XiDtNn82qUEw2fa9pPfiP8DcnG+u7Sc7cK/6kHa0P5XFn7dNgGdL8c6wPb+95VeYADRyj04BTi4ax7YfITWoy0sln5Nj7oyBwFu2Ly4SbLfantDg+c3A+cDTwMcqjtWK70RSI2rh0vm277X9+/YEnnuyWiS1vPnqK+05NYQQQuhxolHTA9j+G+m92jAnNZOW1x4LDCo+gNt+HTgJuAcYnRscABcCv5I0TtIwSR9oo8o+pWEtF9bLmL8hn5UbXwAjgBmSxkr6qqTV23m5tTwCfLDBvHtVDM3pUyNfH+DTwMHAb4FxuddhXk6v9AZwqO1dyCsClhqa/YBRtncCXiX1lBRetf1RUg/KLxq8hsLC65a0ObCx7YdI3+QPLuW7GNgOOBn4Xk6bBLwAPCnpckmfaWfdheJ5mwD0y72HAL8GTpH0gKSf5V67DpO0MnAQi3ojepXfR1KjvbA9MLGiiJacXngAmJ97+io1+ozsUKWesj4VMX6tdD29gP2AW0j3r7ni3FrxbU963+sZWqp3XLUMtkfZbrLdtOo661bLEkIIISw3olHTcwhAaV7Cp4Df234VeJA0xAUA238A/g38spR2B2m1uktJH5AflVRvGNJs2/3zT61NAodKmpHrH16q6zSgiUU9DH9s53XWUh7/5irHy2kTSvH3tz27Rpm35yFzU4CVSrFOAXrXiOHnkiYDfwI2JQ1DAviH7fvy778F9iydd23pz4/XiKWW8nUfSWrMAIym9CHZ9juk4Wi32345py0APgkcDswERuQhYe11JKmR/A4whjQEDtutpOfqHOC9wMOSPtSB8nvlBkELqUfjVzl9Xvl9BP6njXKKlRnLfkb13pBGn5G2zK6I8eLSsUGkhvLrwI3AoXlIZiPxLaQ0R+0xSeeXkkeU6q3WaAshhBBWKNGo6QEkbQ0sAF4kfUhdF5iS56fsyZLfAL+Tfxay/S/b1+QNVR8mzcXpjBG2+5F6C64q98jYnm37ItK31B+WtEEn6wLYGSjm07zMouFukD5Q152jI+mE0jfbRU/V/BzvO6QhRsUH4neAlZUm3BfnfBY4mjQnZdf8AfYFoLjuyg/TbuD3RpSvuxkYkt/3m0n3ttw7Uu19t+2HbJ9Bapwc1p7K8/yrvsBdud4jWbwxNdf2GNvfIDXmPtWe8rNy4+Wbtt9s4JzpwK4Vabvk9IVs3016jyqHfjVqWpV6GtUM7J/v20RgA1IPX1vxTSNdS5FnN+BHpL/3IYQQQqgiGjXLuNyjcjEwMn/obga+bLu37d7AVsABxRyOGmXsW5rjsTZp2NXTXRGf7TGkb9iPzeV/ujQkqy+pMfbvztQhaR/SfJpLc9J44JhSPccCVYfglOK8sPTB+blG6rX9YOmcm0kfKl+0/VYeMrRlKfsWkopemGbg3tKxwaU/H2ikbgBJh5F64a5VmoS/pu1NS+990VCpdf4H8vDAQn/g743WnzUDw4s6bX8A2FRplbM9JK2f61qVNPytveV31NnAWUWDOc8jG0Kph7LkdBYNyWuvu4HVJH2lSJD0kfxM1iRpHdIXDluU3q8TWPILiGrxXUhqvO5eSqv59zuEEEIIsfrZsqoYjrMK8DbwG+B/c8PkQNKqTwDYfk3SvcBngOtqlLcrMFLS26SG7GW2H+7CeE8DrpF0KfAF0jCn13PsR+dhUO01WGnFpzWAJ4HDvGjls1GkYXSTJJnUqPpB6dy98v0r/Mz2DR2IodLVwB+UluJtBR4vHXsMOFZppblZpEUcCqtJepB076t9qC0bKukYYE3SYhD72p4j6QTSHKqyG0nD0H5ao6xVgHNzz9QbwBxKcz5quERSMe/nH6QV1A6qyDOW1Jj6J3BRbly+B7g1x7TU2b45L5Rwf34G/gMcY/ufVfLeJmlORXJDz4htSzoU+IWk75Pu41PAf7cR4ueAuysWTrgJOFvSavXis/280sp3Z+VrfJHUE1meU1Q8J4VDbD/VRkwhhBDCckt2e0fDhBDKJPUGbrG9Q5VjTwFNbnsJ6xCWmqamJre0tLSdMYQQQljKJE203dR2zvaJ4WchhBBCCCGEHi2Gn62glHYn/01F8vw8KfndiuE40gaOZffVWXFtmZSH/SzRS5OP9a5MkzSMvIJYyfW2T+/y4KrIy3TvUZF8vu3Ll2KdGwB/rnJov2K1tu60rMcXQgghhPpi+FkIISzn1uuzrfc564LuDqNL3HT4J7s7hBBCCJ0Qw89CCCGEEEIIoYpo1IQQQgghhBB6tGjUhBBCCCGEEHq0aNSEFZ4kSzqv9PokScMr8kySdG1F2hWSXs8bmhZp5+fy3pdfL5DUWvr5fp04VpF0pqRZkqZKekjSQfnYupKukjQ7/1wlad18rHeu85ulskZKGiLpwlzvdEnzSnEcnuN/Mr+eJGm/injeL+ktSV+tSF9L0iU5jmmS7pG0W6ns5yU9W3q9ao3rHZbPn5zz7SZpbP79CUmvlMrYvVZMkh7MeZ6WNKd0Tm9JcyvqHCJpZP69n6TxOe9jkkbVeW8G5HgelfS4pHNr5a0479p8fUMbyd9e5esJIYQQVmSx+lkIMB/4nKQzqu0nI+lDpC8A9pa0pu3XSoefAA4GfivpPcBA4NnS8Xm2+zcYx0+BTYAdbM+XtBFQ7Fz/K2Cq7S/mmH4CXMaiVdReBL4t6RLbbxYFFivJlfbSWRiLpEHAybZvkDSQtKlp31I8RwB/JW0Yekkp/TLShqh9bb8jaWvgQ0XZuUE413bND/6SPg4MAnbJ1/o+YFXbh+bjA4CTbA+qOHWJmIoV+yQNIe0JdGKpnlohAFwAjLB9U867Y73MwATbgyT1Ah6VNNb2fXWucWNgd9tbtlFuCCGEEDopempCgLdJH+hrfZt+FGn56zuBz1YcuxYYnH8fANyXy2sXSWsAXwG+WexCb/sF27+TtA2wK6nRUzgNaJLUJ7+eQ1qS+Nj21p09AGxakdYMfBfYTGlne3J9uwGn2n4nx/k327e2s75NgJdK1/qS7ecaOG+JmDphE+CZ4oXtKY2cZHse0Eq+X5LWlPRrSQ/nnpyDc9Y7gQ1zT9BeuVeoKZ/zPqWNWZG0fe6Va829On1z+jGl9EskrZTTj5M0U9JfWHJp7oUkHS+pRVLLm6++0s5bE0IIIfQs0agJIbkQOFp5SFeFwcB1pAZMc8WxWcD7Ja2fj42uON5Liw8/G0x12wBP2361yrHtgFbbC4qE/HsrsH0p35nAd4sPv+30SeD3xQtJmwMb234I+B2LGm7bV8bSQXcCm+cP57+UtE9bJ9SJqaNGAHdLul3SUEnrNXJSfq/7AvfkpGHA3bY/QuqpO0fSmqQG8Gzb/W1PqFPk10j7BPUHmoBncu/gYGCPnL6A9HxuAvyE1Jj5BOnZqMr2KNtNtptWXafaYx1CCCEsP6JREwKQGxNXAd8qp0v6CDDH9t9JPSG75A+1ZWOAI0k9GJUfXuflD7XFz3UdCE9AtQ2lFku3/STwEKlnqVHnSPob8Fvg56X0I0kNB0gNtcrGXKfYnkvqfTqe1Mt0XR4+Vk9XxeQcw+XAh4DrSb1sf5W0Wp3z9pI0GXieNJTv+Zx+APB9Sa3AeGB1YIt2xPMA8ENJpwBb5p6g/Uj35+Fc7n7A1qRnbLztOXmYYUeepxBCCGG5E42aEBb5BfAlYM1SWjPwwTxUaDawDnBYxXmjSUPD7iqGZHXAE8AWKi06UDIN2DnP2QEg//5h4LGKvD8HTqHxv9snk3qJTgWuLKU3A0Pydd8MfDgPi5qWf+/0vx22F9geb/vHwIkseV8r1YqpnnlafKGC9wIL503Zfs72r20fTBo2uEOdsibY3gnYEfi6pGJ+koDDSg3XLWxXvi/k8ov7tnophmtIvTrzgDsk7ZvLvLJUZj/bw4tT2rjmEEIIYYUTjZoQMtv/IvUEfAkWNhyOAHay3dt2b9KiAM0V5z1NGoL0y07U/TppMYALig/hkjaRdIztJ4BHSQ2PwqnAI/lYuZzHgemkSfiN1v0OcD7wHkkHSuoHrGl709J1nwEcaXs20AL8RHkWvqS+pXkkDckrj5UbJP2Bv9fLXyumNqr6C3BMLqMX8HlgXH79SUmr5N83BjZg8UUeqrI9M9d9Sk66A/hm6X7sXOPUp0i9LwCHl65ta+Bvti8gNdZ2IvUKHi5pw5znvZK2BB4EBkjaIMd+BCGEEEKIRk0IFc4D3pd/3xt41nb5g+49wHZ5bsNCti/JH/grVc6pObNO3aeShmJNlzSVNMdlTj72JWBbpaWOZwPb5rRqTgc2q1PPEmwb+BnwPVKjbWxFlhtZ1Jj7MrAx8ISkKcClQCOT/MvWAq5UWmp6MmluyPA6+duKqZZvk1a2ayWtmna97WIuzAHAVEmTSA2Tk0tDytpyMWk1vK1IvXSrAJPz+/bTGuecS+rhuZ9FzxikuTNTc4wfBK6yPZ30PNyZ789dwCa2/0m6Tw8AfwIeaTDeEEIIYbmm9FkmhBDC8qqpqcktLS3dHUYIIYSApIm2m7q63OipCSGEEEIIIfRosflmCO8ySWOBrSqST7F9R3fEszRJ2oA0P6TSfrZffrfjaYukA4GzKpKfLDYFDSGEEMKyKYafhRDCcm79Ph/0gLMv6+4wOmzsYXt2dwghhBC6SAw/CyGEEEIIIYQqolETQgghhBBC6NGiURNCCCGEEELo0aJRE8K7QNLGkkZLmp33ZrlN0raStpd0t6SZkmZJ+lFpE8chkuZIejQfu0PS7qUyr5D0ZGkPnPvr1F+U1SrpcUlDS8eGS3q2Yj+d9SQNkHRLlfOLn+3ysW3z9Twh6TFJv5M0uJRvrqQZ+ferasQ3QNIr+Vofl3RujdgXqzsfHyrpDUnrltLWkHS1pCmSpkq6V9Ja+dhmkm7K93S2pPO1aMPTAZIs6TOlsm6RNKDOvR2U456U39uvlo59Mdc/LR87KadL0qk5hpmSxknavnTeUzn24np3l9Rb0rz8erqkq5Q3Dw0hhBBWdLH6WQhLWW6kjAWutH1kTusPbARcAXzd9p2S1iBtKPkN4MJ8+nW2T8znDATGSBpo+7F8/GTbNzQYynW2T8wrks2QdIPtf+RjI2yfW86c21ZLnF+RZ3XgVuA7tv9QinOO7f759XjgJNttbZQywfYgSb2ARyWNtX1frbpLmoGHgUNJ9xPSppsv2N4xx9APeCu/F2OAi2wfLGklYBRpw9KT87nPAMOAP7QRL7lRMQr4qO1nJK0G9M7HDgL+GzjA9nP5Xn0hn3oCsDvwYduvSzoAuFnS9rbfyHkG2n6pVFdvYLbt/jnuu4DPA1e3FWcIIYSwvIuemhCWvoHAW7YvLhJstwLbAvfZvjOnvQ6cCHy/WiG2x5E+QB/fmWDyUspPAJt0ppzsKOCBokGTyx9ne2on4psHtAKbtpVXUh9gLeBUUuOmsAnwbKnMGbbnA/sCb9i+PKcvAIYC/5UblQCTgFckfaKBcNcmfTn0ci5vvu0Z+dgPSI255/KxN2xfmo+dAnwzv+fkZ+B+4OgG6izifog690jS8ZJaJLXMf/XfjRQbQggh9FjRqAlh6dsBmFglffvKdNuzgbUkrVOjrEeAD5Zen1MaotTQN/aStgBWByaXkoeWyhlX49TykLLW3KNS69o6TNL6QF/gnjbqhtSQuRaYAPSTtGFO/zVwiqQHJP1MUt+cXu2evwo8DWxTSv4ZqaFUl+1/ATcDf5d0raSjJRX/rla9N/m9XTO/12UtOb7CuHytD1YpY3VgN+CPdWIbZbvJdtNq66zX1qWEEEIIPVoMPwuh+wiotVFUrfTKMWHtGX42OA8N6wd8pTTMCaoMP6ui2vCzBqtuyF6SJuf4zrT9fL26syOBQ22/I2kMcARwoe1WSVsDBwD7Aw9L+ji17/li6bYnSELSXm0FbfvLknbM9ZwEfAIY0sD11o2BiuFnWR9JraRG3w22JxNCCCGE6KkJ4V0wDdi1Rvpim0/lD+Jzbf+nRlk7A4/VONaW62xvD+wFnCdp4w6WU1br2jpigu2dgB2Br+d5RzVJ2on04f4uSU+RGjgLh6DZnmt7jO1vAL8FPkX1e74OsDlQ2XNyOmluTZtsT7E9gtSgOSwnV703uWfotfxel+0CTG+jqtl5rtI2wMckfbaR+EIIIYTlXTRqQlj67gZWk/SVIkHSR4BZwJ6S9s9pvYALgLOrFSJpH9J8mkurHW+U7QeA35Am03fWNcDukj5dJEj6ZO656Gh8M4EzSPNO6mkGhtvunX8+AGwqaUtJe+RhbOSVzbYD/g78GVhD0hfzsZWA84ArivktpTjuBNYHPlwrAElrVayM1j/XQ76Gs4vGo6TVJH0rHzsHuKAYRpefgT1J97NNtv9Jmnv1g0byhxBCCMu7aNSEsJTZNmllrk8oLSE8DRgOPAccDJwqaQYwhbSK18jS6cVckpnAD4HDSiufweJzalrzB/hGnAUcJ2nt/HpoRTm9ScNT51eJZeEyw3lS/yDgm0rLE08nDb16scE4arkY2FvSVrXqJvXMjK04b2xO7wP8RdIU4FHSfJUbS+/FEZJmATOBN0j3tprTgc3qxCnge8pLVgM/IQ89s30baRW7P+X3fCKLhvz+H+m9npLf+x8BB+f72ajfkxpobQ6RCyGEEJZ3Sv/HhxDC4iR9G9jU9ve6O5bQOU1NTW5paWtF7RBCCGHpkzTRdlPbOdsnFgoIISxB0q9Iq3d9vrtjCSGEEEJoSzRqQliOSDqOJefK3Gf7hPaUY/tLXRfVIpIOJA19K3vS9qFLo76uJGkssFVF8im27+iOeEIIIYSwSAw/CyGE5dx7+2zv/c6+trvD6LDrD9upu0MIIYTQRZbW8LNYKCCEEEIIIYTQo0WjJoQQQgghhNCjRaMmhBBCCCGE0KNFoyaEZZikjSWNzvvbTJd0m6RtJW0v6W5JM/P+MD+SpHzOEElzJD2aj92R93UpyrxC0pOlPV/ur1N/UVarpMclDS0dGy7p2Yr9Y9aTNEDSLVXOL362y8e2zdfzhKTHJP1OUnk/mrnF/i+SrqoR3wBJr+RrfVzSuTViX6zufHyopDckrVtKW0PS1ZKmSJoq6V5Ja+Vjm0m6Kd/T2ZLOL/YFynFY0mdKZd2ixTfmLMe9kqSJkvYupd0p6YjS651zmQdWnLsgX8tUSddLWqPW+xdCCCGsKKJRE8IyKjdSxgLjbfexvR1pk8iNgJuBM21vS9rxfnfgG6XTr7O9s+2+wJnAGEkfKh0/2Xb//LM79V1nuz+wBzBM0ualYyNK5fS3/e9a55d+pktaHbgVuMj2NrY/BFwETCvykTbMPDq//mKd+CbY3hnYGRgkaY96dZeONZM2wCyvvPZt4AXbO9reAfgS8FZ+L8YAv8/3dFtgLdLmnIVngGF14lzI9gLS+3WhpFUkNadkX18R3735z7J5+Vp2AN4EvtZInSGEEMLyLBo1ISy7BgJv2b64SLDdSvpAfZ/tO3Pa68CJwPerFWJ7HDAKOL4zwdh+GXgC2KQz5WRHAQ/Y/kOp/HG2p3YivnlAK7BpW3kl9SE1Sk5l8UbDJsCzpTJn2J4P7Au8YfvynL4AGAr8V6mnZBLwiqRPNBjvg8D9wHDg58DCZbdzI+pwYAhwQG4EVjMB2KbGNR4vqUVSy/xX/18jIYUQQgg9VjRqQlh27QBMrJK+fWW67dnAWpLWqVHWI8AHS6/PKQ3JurqRYCRtAawOTC4lDy2VM67GqeUhZa2SetW5tg6TtD7QF7injbohNWSuJTUK+knaMKf/GjhF0gOSfiapb06vds9fBZ5m8UbFz0gNpUb9APhv4BrbT5TS9yDt3zMbGA98qsr1rgwcBEypVrDtUbabbDetts767QgphBBC6HmiURNCzyOg1gZTtdJV8bo8/OzoNuobLGka8DfgfNtvlI6Vh58NrHF+5RCweW3U1157SZoMPA/cYvv5Buo+Ehht+x3SsLIjYGFP2NbAOcB7gYfzsL1a93yxdNsTACTt1WDsewOvkBp5Zc3A6Pz7aBbvTeolqZU0PO9p4FcN1hVCCCEst1bu7gBCCDVNIw1Bqpa+dzlB0tbAXNv/SSOXlrAz8FgH47jO9omSPg7cKun2ioZDR0wD9ulkGYUJtgdJ2ha4V9LY3DipStJOpB6du/K9WpXUYLsQwPZcUkNnjKR3SL0kk4DDKspZB9gcmA1sUDp0Omluzdv1gpa0JnA2aWjbryV9yvZtklbKdX1W0jBSw2kDSWvb/g95Tk0jNyaEEEJYUURPTQjLrruB1SR9pUiQ9BFgFrCnpP1zWi/gAtIH5CVI2oc0n+bSzgRj+wHgN6TJ9J11DbC7pE8XCZI+KWnHTsQ3EzgDOKWNrM3AcNu9888HgE0lbSlpjzyMjbyy2XbA34E/A2tI+mI+thJwHnBFntNUjuNOYH3SAg71/A/wO9uPkxYNGJHnzuwPTLK9eY5vS+BG4JAGb0UIIYSwwolGTQjLKNsmrcz1ibyE8DTSpPLngIOBUyXNIM2peBgYWTq9mEsyk7Ri2mG2yz015Tk1rcXSxGBXIT8AACAASURBVA04CzhO0tr59dCKcnqTeoDnV4ml+Nk9DwMbBHwzL5E8nTQp/sUG46jlYmBvSVvVqps09GxsxXljc3of4C+SpgCPkoZ43Vh6L46QNAuYCbxBurfVnA5sVitIpaWlD835imFvd5AaZM1V4ruRtLhCCCGEEKpQ+r86hBC6hqRvA5va/l53xxKSpqYmt7S0dHcYIYQQApIm2m7q6nJjTk0IoctI+hVp0vvnuzuWEEIIIaw4olETQkDScSw5V+Y+2ydUy1+L7S91XVSLSDqQNPSt7Enbh1bLvyyRNBbYqiL5FNt3dEc8IYQQwvIohp+FEMJybqNtdvLgc27t7jAacsGhm3d3CCGEEJaipTX8LBYKCCGEEEIIIfRo0agJIYQQQggh9GjRqAkhhBBCCCH0aNGoCSF0mKRhkqZJmpz3gdlN0nhJM0p7w9yQ8w6XdFKVMuZWSRsu6dmKPWbWkzRA0iuSHpX0uKRzS+cMkTQn551ebFqa00dW1pGP7SzJeSGCcvrGkkbn/YGmS7pN0raSekuaVxHXF9u4R7XqWJDPnyrpD5LWy+l166hVXgghhLAii9XPQggdIunjpA00d7E9X9L7gGITz6Ntd3ZjlBG2zy0nSAKYYHuQpF7Ao5LG2r4vZ7nO9omSNgSmSbq5jTqagXvzn3fkOkTa/PJK20fmtP7ARsA/gNm2+7fjOpaoI5tXlCPpSuAE8macbdRRq7wQQghhhRWNmhBCR20CvGR7PoDtl2Bhw2Opsz1PUiuwaZVjL0qaDWxZ6/zceDkc+AQwQdLqtt8ABgJv2b64VF5rPqd3e2KsU0elB4CdurA8JB0PHA+w9vuXuEUhhBDCciWGn4UQOupOYHNJMyX9UtI+pWNXl4ZOndPB8oeWyhhXeVDS+kBf4J4qx7YGtgaeqFP+HqS9bmYD44FP5fQdgIl1zutTMTRsrw7UUY51JWA/oNyrVKuONssr2B5lu8l2U6913lsnxBBCCKHni56aEEKH2J4raVdgL1LvxnWSvp8PL5XhZ9lekiYD/YAzbT9fOjZY0p7AfOCrtv9Vp+eoGRidfx8NfAEY00Bc7Rl+Vq+OXrmnqTepEXVXA3V0NOYQQghhuRaNmhBCh9leQOoxGC9pCnDsu1BtMadmW+DePKemNR+7zvaJbRWQe0cOAz4raRggYANJawPTSEO8OqVeHbb/Q55TI2ld4BbSnJoLOlFeCCGEsMKK4WchhA6R1E9S31JSf+Dv71b9tmcCZwCndOD0/YFJtje33dv2lsCNwCHA3cBqxeppAJI+UjG8rrN1lK/jFeBbwEmSVulseSGEEMKKKBo1IYSOWgu4Mi95PBnYDhiej5Xn1PypdM6pkp4pfnLaGuU0Sd/J6eU5Na01JulfDOwtaas2Yh1SUe/JpBXOym4EjrJt4FDgE3lJ52n5up7L+Srnu3yrRp3NteqozGj7UWAScGSdOhouL4QQQljRKP3/HUIIYXnV1NTklpbOTnEKIYQQOk/SRNtNXV1u9NSEEEIIIYQQerRYKCCEEDpJ0oPAahXJX7A9pTviCSGEEFY00agJIYROsr1bd8dQz4v/fosLx77Q3WG06YRDN+ruEEIIIfRQMfwshBBCCCGE0KNFoyaEEEIIIYTQo0WjJoQQQgghhNCjRaMm9CiSDpVkSR/Mr3vn1z8t5XmfpLckjZQ0rLTXx4IG9hZB0jGSJkuaJmmSpMskrZePrSrpF3n/klmSbpK0WenczXLarJznfEmr5mMDJL0i6VFJMyTdI2lQ6dx+ksbn+B6TNKpOjOWyHpP04yrpj0s6t3TOEEkjS6+/KGlqvs7pkk7K6VdIerJ0r+5v37vUvSTtnJ+JAyvSLek3pdcrS5oj6RZJx5Wu901JU/LvZ9ap5yBJLfn+L7zXkoZLejafP11Sc+mcqvc2vzdz8vs2S9IdknavOO9wSWPzeU/k97koZ/clIwwhhBBWHNGoCT1NM3AvizYpBPgbMKj0+ghgGoDt0233t90fmFf8bvuCaoVL+iQwFDjI9vbALsD9QDGD+efA2sC2tvsCvwfGKAPGAL/Px7YlbVB5eqmKCbZ3tt2PtIv8SEn75WMXACNyfB8C/q+NezHB9s5AE3CMpF0r0ncGBknao8p1HgT8N3BA6TpfKWU5uXSvetoH5uIZaa5Ifw3YQVKv/PoTwLMAti8vPSfPAQPz6+9Xq0DSDsBI4Jj8Xu1Aeg4LI3JZBwOXSFqldKzWvb0uPxt9gTNJz9WHyvXaPjSX+2XS+1yU06ManiGEEEJXi0ZN6DEkrQXsAXyJxRs184DHJBUbOQ0GftfBaoYBJ9kuPuwusP1r2zMkrQEcBwy1vSAfvxyYD+ybf97IaeQ8Q4H/yucuxnYrcBpwYk7aBHimdLyh5YBtvwZMBPpUpM8DWoFNq5z2g3ydz+W8b9i+tJH6CpKekvRzSQ/kHotdcg/DbElfq3HOZyQ9mHsk/iRpo5w+XNJvJN2deyq+ktMH5B6tsbnX42JJNf/dyg3Lw4EhwAGSVq/Icjvw6fx7M3Bte6655HvA6bYfB7D9tu1fVmayPQt4HVi/PYXbHgeMAo7vYHxIOj6/Ly1zX/1XR4sJIYQQeoRo1ISe5BDgj7ZnAv+StEvp2GjgSKWhYAtI37Z3xPbAIzWObQM8bfvVivSWfN72pMbFQjnv0/ncah4BPph/HwHcLel2SUOVh7y1RdIGwMfIvVOl9PWBvsA9VU7boTLWCueUhjZdXSffP2x/HJgAXEFqUHyM1Fir5l7gY7knaTSpcVDYidTg+DjwP5I+kNM/CnwX2JHUcPtcnXj2AJ60PRsYD3yq4njxnKye63uwTln1tHX/AMjP6CzbL5aSG7235Wej3WyPst1ku2mtdd7b0WJCCCGEHiEaNaEnaSZ9KCX/WR5e9EfScKJm4LquqEzSjvmD52xJgwEBrpY1p7d1vGo1xS+5h+dDwPXAAOCvkio3dCzbS9KjwJ3AmbanldInA88Dt9h+vk4ZtZSHSB1dJ9/N+c8pwIO2/2N7DvBGjUbZZsAdkqYAJ5MagoWbbM+z/RIwjtSYAXjI9t9yz9e1wJ514qn3jGB7MtA7p99Wp5zOGippBqnRNLziWKP3VnWOhRBCCKEkGjWhR8i9EfsCl0l6ivSBuGhoYPtN0jfn3wVu7ERV00jzS7A9Jc9fuB3oBTwBbClp7YpzdgGm53ObygckrQNsDsyuUd/OwGPFC9vP5eFuBwNvk3oEainm5+xq++KK9J1IPRtfl9S/xnXuWiW9pjy0rFXSZaXk+fnPd0q/F69XlnR60SuR0/8PGGl7R+CrQHl4WGXDz22kV8a3EnAYqZfnqVzXQVXer5uBc+n40DNo+/6NyPOmBgNXVRkG14jFno0QQggh1BaNmtBTHA5cZXtL271tbw48Sfrmv3AecIrtlztRzxnAuSqtaEZq0BRzV64E/jd/gEbSF4E1gLuBPwNr5LTiQ/Z5wBW2X6+sSNJOwI+AC/PrTxYTyiVtDGxAnsjeEXmY3hnAKTWu8+xcD5JWU50V4XJ5B+behS+3I4ZhpQn4AOuy6JqOrch+sKTVcwN2APBwTv+opK3yXJrBpCFs1ewPTLK9eX5GtiQ1cA+pyPdr4LRG5yzVcA7wQ0nbAkh6j6TvVGayPYY0PLHyWuuStA9pPk275jmFEEIIK6po1ISeohkYW5F2I/DD4oXtabav7Ewltm8jrUJ2e56Yfj9pjs4dOcsPgDeAmZJmkVZaO9QZcChwRD42M+f9YamKvfIk+Rmkxsy3bP85HzsAmCppUq7v5A4OHSu7GNhb0lZVrvNC4E+SppF6uVYuZSnP+2hVXpa6CwwHrpc0AXip4thDwK3AX4GfFosYAA+QVgObSmrIVj4HhVrPyFHlBNvP2D6/oxeQy5hMWj3uWkmP5dg2qZH9NOA7pQUOat3bwfn1TNIzc5jt6KkJIYQQGqD0OSyEELqPpOHAXNvnVqQPIK3SNqjaeaExTU1Nbmlp6e4wQgghBCRNtN3Uds72iZ6aEEIIIYQQQo+2cttZQlj+SBpGGjpWdr3t06vl7y6SDgTOqkh+0vah3RHP0mJ7eI308aSlmRcj6UGgcmW4L3RynswSJB0HfLsi+T7bJ3RlPSGEEELonBh+FkIIy7lt+vT32Wf9qbvDqOtzh7+vu0MIIYTwLojhZyGEEEIIIYRQRTRqQgghhBBCCD1aNGpCCCGEEEIIPVo0asK7RtKCvA/HNEmTJJX37ijynC/p2SJd0vaSZkrqVcpzq6QjJW0k6ZZc1nRJt9Wpu7ekeZX7g0gaImlkzjM8192ay2sunf8xSQ/mY4/lvMeVynpT0pT8+5k1YhgiaU7ep2aWpDsk7V46Pl5SU+l1b0lT8+8DJL1SEf/+7X8X3n0V93WWpDGStqvI835Jb0n6aintGklfL73eTdJkSStL+q98vydLmirp4Dr1XyHp8Pz7eEkz8nmPSxopab024i+e26mSrpe0Rvm9qbjOk0p1PpnPe1zSj0v5Fnufc9oASbfk36s+123VGUIIIazIYvWz8G6aV+wsL2lD4BrSDvM/zmnvIW1e+Q9gb2C87WmSxgDDgFMlHQKsYnu0pEuAu4qNFCXt1Eb9s0s725PPqcwzwva5kvoCEyXdYPst4Erg87YnSVoJ6Gd7OnB5LucpYKDtyg0lK11n+8R8zkBgjKSBDW6yOKEH79cyotiDRtJg4G5JO9qek48fQdp0sxm4JKcNBR6QdAPwMjAS+AawMel52MX2K5LWAt7fjliOtt2itOnlGcBNwD518pef26uBrwFjGqjnZNs3SFodmC7pKttPNnDeabTvuQ4hhBBWeNFTE7qF7ReB44ETtahlMZC0M/tFpA+3hdOAIyT1J+0sXyynuwnwTKnMyV0Y3yzgdWD9nLQh8M98bEFu0HS2jnHAKNJ96BL5m/srJd0p6SlJn5N0du7V+KOkVaqcs5akP0t6JOc7OKf3zr0MV+aejRskrZGPPSXpLEkP5Z9t2nHd1wF3AkeVkpuB7wKbSdo053sBOBc4m9SQmGz7XtJ78R9gbs43t8HGQmUcbwLfA7aQ9OEGT5sANHyt2er5z9cazN8lz7Wk4yW1SGp55dWXO1JECCGE0GNEoyZ0G9t/Iz2DG+akZuBaYCwwqPgAbvt14CTgHmB0bnAAXAj8StI4ScMkfaCNKvuUhm5dWC+jpF2AWbnxBTACmCFprKSv5m/fu8IjwAcbzLtXxfCzPjXy9QE+DRwM/BYYZ3tHYF5Or/QGcKjtXUgNy/NKDc1+wCjbOwGvknpKCq/a/iipB+UXDV5DYeF1S9oc2Nj2Q8DvgMGlfBcD2wEnkxogAJOAF4AnJV0u6TPtrHsh2wtyeW2+B5JWBg4CGt0L5xxJraQGyujSs9SWes91+RluJTX2qrI9ynaT7aZ119mgwapDCCGEnikaNaG7CSAPBfoU8HvbrwIPAgcUmWz/Afg38MtS2h3A1sClpA+lj0qqNwxptu3++afW5olDJc3I9Q8v1XUa0MSiHoY/tvM6aymPf6u2aVQ5bUIp/v62Z9co8/Y8ZG4KsFIp1ilA7xox/FzSZOBPwKbARvnYP2zfl3//LbBn6bxrS39+vEYstZSv+0hSYwZgNKVeOtvvkIaj3W775Zy2APgkcDgwExghaXg7668VSzW9cgOiBXga+BXV3ysq0k/Ow9Y2BvZTaf5UPW081+VnuD+p0RdCCCGs8KJRE7qNpK2BBcCLpA+p6wJT8vyUPVl8CBrAO/lnIdv/sn2N7S8AD5Pm4nTGCNv9SL0FV5V7ZGzPtn0RsB/wYUld8fX3zkAxn+ZlFg13A3gvUHeOjqQTSt/cF9/oz8/xvgO85UU77L4DrKw04b4457PA0aQ5KbvmD8ovsGjIVOWHdzfweyPK190MDMnv+82ke9u3lLfa+27bD9k+g9QoOqyd9QOQ50ftWIqlmnmlhsQ387C1yvcKarxftucC41m8QVjXUniuQwghhOVaNGpCt8jfPF8MjMwfupuBL9vubbs3sBVwQDGHo0YZ+5bmeKxNGnb1dFfEZ3sM6Zv5Y3P5ny4NyepLaoz9uzN1SNqHNJ/m0pw0HjimVM+xwLg24ryw9IH7uUbqtf1g6ZybSY3JF22/lRcv2LKUfQtJRS9MM3Bv6djg0p8PNFI3gKTDSL1w10rqB6xpe9PSe180VGqd/4E8PLDQH/h7o/WXylkl1/WP9s5byQ2Vf0raL5f1XlLD/N7KvHnY2m5ArZ61yvxL7bkOIYQQllex+ll4NxXDeFYB3gZ+A/xv/gB3ILBwOV/br0m6F/gMcF2N8nYFRkp6m9RAv8z2w10Y72nANZIuBb5AGub0eo796DwMqr0GS9oTWAN4EjistPLZKNJwo0mSTGpU/aB07l75/hV+ZvuGDsRQ6WrgD5JagFbg8dKxx4BjlVaam0VaxKGwmqQHSfe+slet0lBJxwBrkhaD2Nf2HEknkOZQld1IGob20xplrQKcm3um3gDmUGduSRVXS5oPrEYabldzOeg2fBG4UNJ5+fVPKoYEniPpVGBV4M8svmLarZLeyr8/QJpHU6j6XEvq3cE4QwghhOWeFo1MCSGERfKH6Fts71Dl2FNAUwNLWIdlQFNTk1taWro7jBBCCAFJE203tZ2zfWL4WQghhBBCCKFHi+FnYbkiaUfSsLay+bZ3exdjOA74dkXyfXVWXFsm2X4KWKKXJh/rXZkmaRhpE82y622f3uXBVZGX6d6jIvl825c3cO4GpCFilfYrVl0LIYQQwrIrhp+FEMJyrt/W/X3xT+/q7jCWMPDoeiuwhxBCWB7F8LMQQgghhBBCqCIaNSGEEEIIIYQeLRo1IYQQQgghhB4tGjXLOEkL8s7v0yRNkvQdSe+pyHO+pGeLdEnbS5opqVcpz62SjpS0kaRbclnTJd1Wp+7ekuaVdp9vlbSqpCGSRuY8w3Pdrbm85tL5H5P0YD72WM57XKmsNyVNyb+fWSOGIZLmSHpU0ixJd0javXR8vKSm0uvekqbm3wdIeqUi/v0buOdPSXpfW/m6Wr6WGZImS3pc0khJ61XkOVSSJX0wv149592xlOd7ki6W9B5JF0iamu/zw5K2qlN/zeuWdJOkByrS+uWYi/d3lKQDS/d6br6eVklX1Si3eI8ezWX8uJR+S0XeKyQdnn9fVdIvJM3Oz8VNkjYr5bUW7R+DpJMkDc+/l5/Z4mex+1xR70cl3ZOv5XFJl0lao/z3oJS38nncOcdyYEW+mvHl18fk56D4e39ZEWPpOSli74q9ikIIIYQeLVY/W/bNs90fQNKGwDWkHeCLD3/vAQ4F/gHsDYy3PU3SGGAYcKqkQ4BVbI9W2kTxLtvn5/N3aqP+2UX9BS3c8H6hEbbPldQXmCjpBttvAVcCn7c9SdJKQD/b04HLczlPAQMb2OvkOtsn5nMGAmMkDSxtWlnPBNuDGsi3rDjadoukVUm73d8E7FM63kzatf5IYLjtNyT9N/BLSXsDHyBtYtoEDM6vd7L9Tv7Q/1p7A8ofpncB5krayvaT+dAFpPf+ppxvR9tTgDvy6/HASbbb2iBlgu1BktYEWisbMzX8HFgb2Nb2AqUV58ZI2s1p9ZP5wOcknVHj+Rph+9y2KpG0EXA9cKTtB5Qe/sNy3Y0o3q9m8n3JasYn6ZPAUOAg28/mvzvHAhsB/87Zjm7gvoYQQggrjOip6UFsvwgcD5yoRS2LgaQd2i9i8V3dTwOOkNQfOBMolhPeBHimVObkLoxvFvA6sH5O2hD4Zz62IDdoOlvHOGAU6T50CUkbSLoz9xZcAizRasv5Pirp/pzvfkn9cvqQ3FPwx/wNetHg7J2/2b8yf+t+g6Q1GonJ9pvA94AtJH04l7cWacniL5EaNUXeP5Lu8xeBEaTGzv8jvdf/tP1OzvdMTm+vw4A/AKPL9bLkszSlA2UvZPs1YCLQp16+fA+PA4baXpDPvZzUUNg3Z3ub9JwM7UxMpL83V9p+INdj2zfYfqGtE/Pf0cOBIcABklYvHa4X3zBSY/DZXOcC27+2PaM9gUs6XtL/b+++4+Sq6j6Of74UaaEjglKWjvSEpSiCoYPSi0mMgDwo6oMFlKIGNaAICIh0iCgQwFCDIEpRyhNADGxCKqGFBKQoTXoMEH7PH+cMubmZ2Z3d7GZ3Nt/367WvnTn33HvPuXeyub85rUVSyxtvelZqMzPr3RzUNJiIeJp031bOSYOAEcBNwF6SFs353gWOBUYB1+SAA+AC4HeS7pE0RNIn2zjlOoVuLhe0llFSP+DJHHxBesB+XNJNkr5ReqibF2OBDevMu32pm1G1B+afAfdHRF/gFmCNGsd6DNgh5/spqbWgYmtgMLAFKZisdEHaABgWEZsBbwL/W2e5yQ/s45ld1/2A2yPiCeC1fL0rjgZOAT4eEZV1eq4D9s71PktS33rPXVL5jI1gzsD5bOBuSbdJOqa1Llz1UForZltgck6a494B++T0dYFnI+LN0iFagI0L7y8ABktatsrpjikc+55WirUJKdCqZUCpjMUpKrcDpkXEVOBe4AulfWuVb2PSZ7w1VxfOe0a1DBExLCKaI6J52WVWbONwZmZmjc1BTWMSpHEFpAelP+YHvNHAbpVMEfEnUneVCwtpdwBrA78lPSw/Iqm1xSKmRsQW+afW4pHHSHo8n39o4Vwnkx7y7gS+DNzeznrWUmxJqbbQUjHtvkL5t8gPmGU7AFflMv8ZqNWasSxwvdKYnbOZ8wH6rxHxakTMAEYCn8vp/4yIB/Lrqwrp9SrWdRCptYT8+6MAIyJeAO4mtdhV0p4jBVU/Aj4E7pK0c7tOnrpfrUsK+p4APpC0ST7+ZcCnSd2z+gP/kLRYe46fbS/pEdLn5LSIqAQ1c9w7UsAJ6ZpUu+9zpOd/E8OB71bJe3bh2Dt2oMwV15bKWOwSVvN+1VE+IHXpy4HLVEkDCpsGF8573DyU38zMrFdwUNNgJK0NzAJeAvYgPWhPzONTPkfpwYn0MPthMSEiXouIP0TEIcDDpIf6eXF2RGxAGsMxvNgiExFTI+IiYGdg8/xt/LzqC1TG07zK7O5uACsArY7RkXRU4VvuSkvVXA/JVfL9HLgnIjYB9gaKLU/l/aON9DblsRSbAlPyddsJuDTf6+NIrQTFoKfavZ4ZEbflB99fklp72mMA6fpOy+dtYs6uby/krlH7krpUbdLO40MKXvpGxJYRcXEd+Z8C1pRUHtfSDyh3cfwNqbveUh0oF6RWoy3bu1O+dwcCP83X7Txgzyplrla+yaS6EBETc7B0G7AEZmZmVpWDmgaSW1QuBs7Pg6EHAV+LiKaIaALWIvXdrzluQ9JOle35AWsd4NnOKF9EjCR9U31YPv4XCw/d65GCsddr7F4XSZ8njaf5bU66F/hK4TyHAa11JyIiLih8y/0CqYve4Hz8PclBUpV8ywLP58N8tXTYXSWtoDTj3H5ApXVmDUmfya8rg8brqeeipIkC/pnHPR0EDI+INfP9Xh2YRistP5L6VYI2pQklNgOeqef8BYOAPQqfsS3JQY2kPSrdHSWtAqzI7OvTZfLYmyuAX+fgAUmHAkuSWquKeV8jdcM7ooOnOx84TNI2lQSlmclWaWO/XYDxEbF6vnZrAjdSCiprlO9U4EwVZnPDAY2ZmVmrHNT0fEvkloLJwN9IXXROyoHJ7sCfKxnzw979pFaEWrYEWiRNAB4ELo2IhzuxvCcDlWmnDyGNqRkHXEnqMjOrA8esjFt4AvgxcGDMnvlsGPAWMF7SeKAPUJzVqjym5qAqxz8J2EHSWFL3vVpB3q+AUyU9ACxc2nZ/ruM44MbCzFRTSA/FE0itSBfRuqtz3kmkb+/3zemDSOOmim4kdeurZWXgT7m73ARSS8r5reQHmCDpufwzkjS+6B+VjZFmPnszP+TvBkzK1/0O4LiI+Fcbx+8sPwL+Czwh6UngYGD/HOyXnQWUp6o+pvS5aKp2kjwhwEBSkPG4pCnA9qTxUa1pz/2ao3wR8RfSzHK3KU2T/nfSFwLF2dOKY2r+1kZZzMzMej1VfwYws3pJ+irQHHna6UJ6E3Br7q5m1m2am5ujpcUzQJuZWfeTNCYimtvO2T5uqTEzMzMzs4bmxTcNpdXorywlz4yIbarl76IyHA58r5T8QCszrvUYEXE5cHmV9OlUGTgv6SbS+KeiE/LMdF1O0migPEvZITGP68y0cc7dgdNLydMiYv+uOmd79PTymZmZWevc/czMrJfbqGmLuPrEO7u7GHPo+7WV285kZma9jrufmZmZmZmZVeGgxszMzMzMGpqDGjMzMzMza2gOasxsvpE0VNKxXXj8/pI+21XHbw9J0yWV18eZ52sg6XZJr0u6dd5KaGZm1ns4qDGz3qQ/UDWokdRbZns8g7SwrZmZmWUOasysS0kaIulxpZXvN8hpX5f0sKTxkm6UtKSkpSVNk7RozrNMbu1YVNJ3JT0qaYKka2qcpwn4JnCMpHGStpd0uaRfS7oHOD23klwh6c587AMk/UrSxNwCsmiV4/aX9H+SrpP0hKTTJA2W9FDeb52OXIOcfq+k5vx6JUnT8+uFJZ2Rr9EESd+o7BMRdwFv1XHOIyW1SGr5z1uvtpXdzMysoTmoMbMuI2lLYCDQFzgA2CpvGhkRW0XE5sAU4IiIeAu4F/hizjMQuDEi3gd+CPSNiM1Igctc8rpAFwNnR8QWEXFf3rQ+sEtE/CC/XyefY1/gKuCeiNgUmFE4d9nmpHWUNiW1kqwfEVsDlwLf6eA1aM0RwBsRsVXO/3VJ5bWNWhURwyKiOSKal196xfbsamZm1nAc1JhZV9oeuCki3o2IN4Fbcvomku6TNBEYDGyc0y8FDs+vDwcuy68nAFdL+grwQTvLcH1EzCq8vy0HShOBhYHbc/pEoKnGMR6OiBcjYiYwFbizjn0qal2D1uwGHCppHDAaWBFYr479zMzMJ47OAQAAIABJREFUFkgOasysq1Vb4fdy4Nu5heQkYHGAiHgAaJL0eWDhiJiU838RuADYEhjTzvEx75Tez8zn+hB4P2avQPwhsIikbXL3tXGS9inuU8g3s7TPwoV9Tq5ShlqrHH/A7L/DixfSBXwntzhtERFrRUTPWj3TzMysB3FQY2ZdaRSwv6QlJC0N7J3TlwZezGNYBpf2GQ6MILfSSFoIWD0i7gGOB5YD+tQ431v52B0WEaMLwUQ9rSpExKzCPj8tba51DQCmkwI1gIMK6XcA3yqML1pf0lIdqpCZmdkCoLfMBmRmPVBEjJV0LTAOeAaojHP5Calb1TOkLlzFQORq4BekwAZSF7GrJC1LasE4OyJer3HKPwE3SNqXNsa6zC+tXAOAM4HrJB0C3F1Iv5TUrW2sJAEvA/sBSLoP2BDoI+k50nikO7q8ImZmZj2YZve8MDPrfpIOAvaNCE9b3Emam5ujpaWlu4thZmaGpDER0dzZx3VLjZn1GJLOA/YEvtDdZTEzM7PG4aDGzHqMiKiry5ikw0lTLBc9EBFHdX6pzMzMrKdzUGNmDSciLmP2dM/Whvf+/T7Tf/Ov7i4GAE1Hr9LdRTAzs17Is5+ZmZmZmVlDc1BjZmZmZmYNzUGNmZmZmZk1NAc11itI2l9SSNowv2/K739eyLOSpPclnS9pSGEF+FmF199t4zzjJY0opV0uaVref6ykz9SRflDpGE2SJhXeby1plKTHJT0m6VJJSxa23yzpwcL7mvWRNFTSsTmfJJ0o6UlJT0i6R9LGheNMl3Rj4f1Bki6v8zb0CJL65nu/eyk9JF1ZeL+IpJcl3Srp8MI1e0/SxPz6tFbOs6ekFklT8j06M6cPlfR83v9RSYMK+xQ/E+Mk/T2nfzWX5ZF8b+6Q9NnSfgdJuinv95SkNwrH+ezcJTQzM1twOKix3mIQcD8wsJD2NLBX4f3BwGSAiDilsgI8MKOwGvy5tU4g6dOkfzM7VFnd/bh8rB8Cl9SRXpOkTwDXAydExAbAp4HbyQtUSloO6AcsJ2mtdtbnKOCzwOYRsT5wKnCLpMULeZqLgU4DqnwWBpXS3wE2kbREfr8r8DykiQcK1+8FYMf8/ofVTiBpE+B84CsR8WlgE9LnreLsfKx9gUskLVrYdlzh/hSDkWsjom9ErAecBozMn7mPRMT++bhfA+4rHOfvdV4bMzOzXslBjTU8SX2A7YAjmDOomQFMkVRZ4GkAcN08nOrLwJXAncA+NfKMAtZtR3o1RwFXRMSDAJHcEBH/ztsPBP4EXMOc9a3HCcB3IuLdfOw7gb8Dgwt5zgR+3NaBcqvOLyU9mFss+uUWhqmSvlljn70ljc4tEn/LAVyldeNKSXfnloqv5/T+ucXqptzqcbGkmn+3JAk4CPgqsFspWAO4Dfhifj0IGEHHHA+cEhGPAUTEBxFxYTlTRDwJvAss356DR8Q9wDDgyA6WD0lH5vvS8uo7r3b0MGZmZg3BQY31BvsBt0fEE8BrkvoVtl0DDJS0GjCL9C18Rw0AriU9CJdbASr2Bia2I72aTYAxrWyvPIy3Vo65SFoGWCoippY2tQDFlpnrgH6S6gnC/hkRnwHuAy4nBRTbAifXyH8/sG1E9CXdm+ML2zYjBRyfAX4q6ZM5fWvgB8CmwDrAAa2UZztgWq7jvcy9iGfl87B4Pt/otqtYVVv3CID8WXwyIl4qJJ9R6DZ2dSu7jwU27GD5iIhhEdEcEc0rLrViRw9jZmbWEBzUWG8wiPSwSv5dfNC/ndTNaBApIOkQSVsBL0fEM8BdpIf+4rfvZ0gaR/pm/Yg60jtajk+QWnzuz0HcB7kr1DwdFojC+1nAGcCP6tj3lvx7IjA6It6KiJeB/+ZucmWrAXdImggcx5zB1M0RMSMiXgHuIQUzAA9FxNMRMYsUyH2ulfK09lkgIiYATTn9L3XUr6OOkfQ4KWgaWtpW7H42eO5dP6IuK52ZmVkv46DGGpqkFYGdgEslTSc9KA8gPxBGxHukb9R/ANxY4zD1GARsmM8xFViG1A2sovKgumtETKojvTWTgS1rbBtA6so0LZeliTq7oEXEm8A7ktYubeoHPFpKuxLYAVijkpC7lo2TdGkh38z8+8PC68r7RSSdUmmVyOnnAedHxKbAN4Bi97BiYFV8Xyt9DpIWJt2Tn+Zrcx6wp6SlS1lvIXWx62jXM2j9HkEaU7MB6X4Nr9INrh59gSkdKZyZmdmCxkGNNbqDgOERsWZENEXE6sA0UotAxVmkQfcdGliQx3AcDGyWz9FEGgBed9evdjofOEzSNoUyfEXSKvmcexTKsSXtG1dzBnBuZbC8pF1ILR9/KGaKiPeBs4GjC2m75wDta/WeLCKGFAbgAyxLHpwPHFbKvq+kxXOg2h94OKdvLWmtfB8GkLqwVbMLMD4iVs/XZ01SILtfKd/vgZMjot7ugNWcAfxY0vqQPiOSvl/OFBEjSd37ynVtlaTPk1r3fjsPZTQzM1tgOKixRjcIuKmUdiOFge4RMTkirpiHc+wAPB8RzxfSRgEbSVq1g8e8RNJz+efB4oY8IcBA4EylKZ2nANsDK5BaTv5RyDsNeLMYALXhPFKwMDF3j/oJsG9EzKiS93fAIu2tWBuGAtdLug94pbTtIeDPpPr9PCIq458eJM0GNokUsJbvd0Wtz8KXiwkR8VxEnNPRCuRjTCAFfCPy/ZkE1PosnAx8vzDBQXFMzThJH8vpA/L7J0if3wMjwi01ZmZmdVBE1Z4cZmbzjaShwNsRcWYpvT9wbETsVW0/q09zc3O0tLR0dzHMzMyQNCYimtvO2T5uqTEzMzMzs4bW2V1LzBqapCGk8TNF10fEKd1RngVFRAytkX4vaWrmOUgaDSxWSj5kHsfJzEXS4cD3SskPRMRRnXkeMzMzmzfufmZm1sttvvqmccf3ag1F6nqrHFvvurNmZtbbufuZmZmZmZlZFQ5qzMzMzMysoTmoMTMzMzOzhtZjghpJs/IaDZMljZdUXNehkuccSc9X0iVtLOmJykKCOe3PkgZK+oSkW/OxHpX0l1bO3SSp6mrvkhaR9IqkU0vpe0l6pHD8b0gaUlh7Ylbh9XdrHHtors84SU9KGilpo8L26ZJWKrzvL+nW/Pqrkl4urXexUbXz5Py3S3q9sn9n1SOX6cFS2iKS/i1pVUmXS5pWOMbfq5T/MUnHFPbfQNK9edsUScPK9c/v95M0Ie8/UdJ+hW2X52u7WH6/ktIq822S9HY9+Tpbvt8T88+jkn5RKX8hzzGS/itp2fx+5Xx9VynkuVDSDyUtKenqfLxJku6X1KeV89esd/58jCilbStpdOE+DZV0eOFev5fPPU7SaTWOW/wcPCrp64X080t575XUnF8vK2m4pKn5Z3jhmjRJCknfKex7vqSv5tdVP5Ot1H1PSS25jo9JOjOnD5V0bClv+d/s/rksGxbSWi1ffv/9wud6vKRfS1q0cI6JhfKf21r5zczMFgQ9afazGZVVxyWtTFrhfFngZzltIWB/4J+kxRDvjYjJkkYCQ4AT80PtohFxjaRLgL9WFtmTtFkHy7Ub8DjwJUk/jojIDxfDgK0j4rn84NkUEY8Dp+TzvV1YRb01Z1fW5pA0ALhb0qYR8XId+14bEd+usx5nAEsC36gkdFI9RgGrSWqKiOk5bRdgUkS8KAnguIi4oVb5lVaQf1zSDRHxT+Bc0nW5OZdh0/KOkjYHzgR2jYhpktYC/irp6bwwIsAs4H+Ai+q4Pj3FjhHxSg4+huWf4mr0g0iLZ+4PXB4RL0k6nXQtviKpH/A5YEvgWODfEbEppGAReL+9BZL0adIXIDtIWioi3smbrgC+FBHjJS0MbBARjwKX5f2mV+rTxikqn4OVgcmSbqmjWL8jfcYOzec6CbiU2TPXvQR8T9IlEfFelf1rfSbnIGkT4HzgixHxmKRFgCPrKF/FIOB+0mKqQwvpNcsn6ZukvzvbRsTrSotzfh9Ygtn3r57ramZmtsDoMS01RRHxEunB4dvKT8XAjqRVuy8iPShUnAwcLGkL0qrjlalWVwWeKxxzAh0zCDgHeBbYNqctTQoIX83HnpkDgXkSEdcCd1JaAb0zRMRdwFul5HmuR0R8CFwPDCgkDwRGVN+j6jFeBZ5i9ors5XtXbZreY4FfRsS0nGcacCpwXCHPb4Bj8oNoTZLWkvSgpIcl/byVfHvnlolHJP1N0idy+lBJV0q6W6nFrdLa0F/SKEk35VaIi1VqfawlIt4GvgnsJ2mFfLx1gD7Aicz5b2AYsI6kHUkP4N+OiPdJ1/H5wjEfj4iZ9Zy/5MvAlaTP5j6F9JWBF/OxZ+WApsPyv/upwJqt5ZO0LiloK96rk4HmfI0AXgbuYs6AsCOOB06JiMdyGT+IiAvr2TEHptsBR5D+TRS1Vr4hwLci4vV8zvci4rSIeLM9BZd0ZG5hann17dfas6uZmVnD6ZFBDUBEPE0q38o5aRDpQfkmYK9KV4yIeJf0gDsKuCYinsz5LwB+J+kepe5Un2xvGZS6te0M3JrPPSif8zXgFuAZSSMkDa73YbUOY4EN28yVDNCc3c+WaHuX2TqxHiPID225tecLwI2F7WcUynh1eWdJawCLA5XA82xSi9VtSt2tlqtyzo2BMaW0lpxe8SzpW/JD2ij/OcBFEbEV8K9W8t1P+va8L3AN6YG3YjPgi8BngJ8WPm9bAz8ANgXWAQ5ooywfyQ+x04D1clLl38B9wAa5ZaMSWH6LdM2fiIhROf/vgRNywPYLSevRMQOAayn8G8jOJrWw3aTUbXHxDh4fAElrA2uTAlwofb6ByvSPGwHjImJWZd/8ehxz3v/TgB/kVqSyVj+TBZsw9+es6JhSGYt/Z/YDbo+IJ4DXcita0Vzlk7Q00KcSrLfinsJ5j6mWISKGRURzRDSv2GeFNg5nZmbW2HpsUJMJIHe/+ALwx/ygN5rUPQOAiPgT8DpwYSHtDtID0m9JQcIjkj7ezvPvBdyTA6cbgf0rDyAR8TVSwPMQKaj6fUcqWIUKr6stIlRMuzYitij8zGjvyTqjHhHxMNAnd2/aE/hHRPynkOW4QhkHF9IHSJoMPA2cExH/zce7DPg0qQWoP/APlcaWkK5T+fpUS/slqfWmtc/6dsxuWbqylXyrAXdImpiPWXyAvjkiZuQuQfeQghmAhyLi6fzQPYLUNaw9ip+HgaTA/UNgJIVFQiNiHKkl88JS2tqkrocrAA/nrmT1n1zaCng5Ip4htSz0k7R8Pv7JpECj0rp4ezvrVjEgBwQjgG/kYBtKn29S0ArV7/Nc6TkweIjqLZ+1PpPtdXapjC8Utg0iBb/k38WAsFb55qiDpN1z4DJd0mcL+XYsnPfseSi/mZlZr9Bjg5r8re0sUt/zPUjjaybmfvqfo/SAAHyYfz4SEa9FxB8i4hDSOIQd2lmMQcAu+ZxjgBVJ3eAqx5+YHyh2BQ5s57Fr6QtMya9fBZYvbFsB6PR+9J1Uj2tID93t6Xp2bURsDGwPnKXCYPeIeCEifh8R+wIfkL4xL5rM7G/uK/oBc3SBioinSN/gf6mSJumUwjfrH2UtF65KvvOA8/MYlW+QWpdq7R9tpLcpf2vfBDyhNCZsPdK4oemk61zPv4G3I2JkRPwvcBXpy4H2GARsmM85FViGwmckIqZGxEWkwHhzpfFR7VUJXraJiHpWiJwM9C22KubXmzP7307FL4ET6Pjfusmkrm7tkq/DTsCl+dodRwreVMo6R/nylzbvKI0RIyLuyMHSJOBjHayDmZlZr9cjg5rconIx6QEySA9WX4uIpohoAtYCdpO0ZCvH2KmyPT8crkPqjlRvGZYhBU9rFM57FDBIUh9J/QvZtwCeaUcVa53zQFILVCUouJfcdSq3EH2F1ArQKTq5HiNI5duJ1KWtbhHxIKmF5Hu5XHto9kxPq5CCyedLu50J/EhSU87XBPwYOKvKKU4htUJVzjek8M06wAPMHvMwuJV8yxbKUR4Lsa+kxfPDbH9SEA2wtdKYnYVI3bjur3khCvJ4jAtJrZP/If0bGFr5LEbEJ4FPSao5/kTSdpVWldzauRHtuL+5zAcDmxX+Deyby4KkLxYe0tcjfQnxer3H76gcqD5CGltUcSIwNm8r5n2MFOju1cHTnQH8WNL6kK6JpO/Xsd9BwPCIWDNfu9VJXQnnaKmrUb5TgYsq3S7zNZ6nrn1mZma9XU8KapbI34pPBv5G6tJyUg5Mdgf+XMkYafal+4G9WznelkCLpAnAg8CluZtULRtIeq7yQ/om/u7SwOqbSQOlFwaOl/R4/hb/JOCr7axvRaVP/pPkoCBmz3z2c2BdSeNJD3FPkb5tryiPqfksNUi6j9Sda+dcx91JXV06pR55kPi7pGv2TmnzGaVyVvvG+XTg8ByA7gZMyvW+g9RVaI6xLrlr1QnAnyQ9BvwJOD6nl8s2mTRWqZbvAUdJepgUuNQyFLg+X8tyi9lDpM/oP4CfR0SlG9KDpLETk0gPtW21RNyjNL34Q6QgvDJb3cAq+97E3APQi9YB/i93l3uE1H3rxlbyL1n6N3A08HxEFAPKUcBGklYlBdyVz86VwODiOJcudgSwvqSnJE0F1s9p1ZxC6jpYVM9nsjLByNHACElTSPdx1Wp5SwYx9/26kepd4crlu4j0N3B0/vv1AOn+PVLIUxxTM7yO8piZmfVqSg0hZtZRkoYCb0eemruQ3h84NiI62kpg1imam5ujpaWl7YxmZmZdTNKYiCgPIZhnPamlxszMzMzMrN160uKbXU5pEcfy7FYzI2KbLj7vEAozVWXXR8QpnXyeLq3f/KpHo4mIoTXS7yWNi5qDpNFAeTa3Q6L6ejydKo/5uavKpp0jrRfUVec9nDxmquCBiDiqWv75raeXz8zMzFrn7mdmZr3c5qtvFHf+oLXZyjvPJ45u92RxZma2AHH3MzMzMzMzsyoc1JiZmZmZWUNzUGNmZmZmZg3NQY1ZDyIpJJ1VeH9snjK6mGe8pBGltMslvZvX+amknZOPt1J+P6u0NssPWynHvXn9ovGSHpC0QZX0hyVtUdhnWUnDJU3NP8MlLZu3NUmaIekRSVMkPSTpsMK+QyUdWyrD9ELZV5F0TT7uo5L+ImnzQl1ekzQtv/5blfpsWiuvpMfyJBuVvMdLurhQ5nH5nBcrLb5ZTK/8HFrzpnYBSW/n3015XSMzM7MF2gI1+5lZA5gJHCDp1IgoL/CJpE+TvozYQdJSpYVOnwL2Ba6StBCwI1BcOHNGRGxB/QZHRIukI4EzSAvPFtMPz+m75vTfAZMi4tBc1pOAS5k9Y97UiOibt60NjJS0UERc1lohJIm0kOUVETEwp20BLFOpj6TLgVsj4oZqx8gzy1XNK2kP4EJJOwCfJC142kxaiHVqRGwhaRHgbmA/0kKuU9t5Lc3MzKwLuaXGrGf5ABgGHFNj+5dJ03bfyewgo2IEMCC/7k9aif6DTijTKGDdKukPAp8CkLQusCXw88L2k4FmSeuUd4yIp4HvA9+t4/w7Au9HxMWF/cdFxH1116AVEXE78CJwKHA2MDQi/lPK8wHwd6pfh5okrSnpSUkr5Vae+yTtViXf5wutPo9UWtwkHZdbxCbkILE95z5SUoukltfe+U/bO5iZmTUwBzVmPc8FwOBK162SAcC1pABmUGnbk8DHJS2ft11T2r5EqcvUAOqzN1BtDZ09gD/m1xsB4yJiVmVjfj0O2LjGcccCG9Zx/k2AMXWWtaOOBk4BPh4Rc819LGlJYGdmX4d1Stdy+2oHjYhngNOBi4EfAI9GxJ1Vsh4LHJVbf7YHZuTgZz1ga1Ir05a5NakuETEsIpojonmFpZavdzczM7OG5O5nZj1MRLwpaTipFWNGJV3SVsDLEfGMpOeA30tavtSqMBIYCGxD6kZV1N7uZ1dLmgFMB75TSl8KWBjoVykeUG3Rq1rplW0VtfLMl4W0IuIFSXcDt5Y2rSNpXC7HzRFxm6Qm2tH9LCIulXQw8E1yF7gqHgB+LelqYGREPJeDmt2AR3KePqQgZ1Q7qmZmZrZAcFBj1jP9htSSURxvMgjYUNL0/H4Z4EDSuJWKa/J+V0TEh2k4SocNjoiWaunAeOA0UqvSAcBkoG8eI/MhQB7Xszkwpcbx+xa2vQqsWtq+NPB6PvZB81CPen2Yf4rmeexMbuVZLb/tA7wl6Sjg6zntCxFxmqQ/A18A/iFpF1LQd2pEXDIv5zczM1sQuPuZWQ8UEa8B1wFHwEcBwsHAZhHRFBFNpEkBBpX2exYYAlzYxeV7HzgR2FbSpyPiKVKLwomFbCcCY/O2OeTWjjOB83LSKGCfwliSA4DxuQvb3cBikr5e2H8rSZ/v9Ip1jdOBq4GfAr8FiIgLImKL/POCpHUiYmJEnA60kLrl3QH8j6Q+AJI+JWnlbqqDmZlZj+aWGrOe6yzg2/n1DsDzEVGczWwUsJGkOVo4Wvlmf4nclari9oioOa1zWyJihtL008eSgq8jgPMkPUVqZXgwp1WsI+kRYHHgLeC8ysxnETFB0vnA/ZICeAn4Wt4WkvYHfqM0DfV/SV3iju5o2TvBOqVr+fuIOLecKQdeWwHbRcQsSQdKOrzKjG9HS9oRmAU8CtwWETPzbHcP5ha3t4GvkK6NmZmZFShivnRZNzOzbtLc3BwtLdV6EpqZmc1fksZERHNnH9fdz8zMzMzMrKG5+5nZAkzSTcBapeQTIuKO7ihPZ5C0KWktn6KZEbFNF593NLBYKfmQvPCnmZmZdSEHNWYLsIjYv7vL0NlyEDFPM5Z18LxdGjTNi/dfeot/n3tvl5/nE9/t3+XnMDMzq8bdz8zMzMzMrKE5qDEzMzMzs4bmoMbMzMzMzBqagxqzBicp8noxlffHShpayjNe0ohS2uWS3q0seJnTzsnHWym/nyVpXOGn6ro2kn4p6fTC+zUlPS1pOUn3SmrO6dMlTSwc79xCWQ4qHbNJ0gxJj0iaIukhSYfVcT32yHkfy+e4VtIahfNMy+njJe1c2O9jkn4jaaqkJyXdLGm1Qlkmlc4zVNKxVY47VtJnWinfR9ejlN5cuR6t7DtXOczMzMwTBZj1BjOBAySdGhGvlDfmBRwXAnaQtFREvFPY/BSwL3CVpIWAHYHiAp8zIqKeQfc/Bx6RdHlETAHOAX4SEa/nhSOLdqxWzhqmRkTfXI+1gZGSFqqyeCU5zybAecA+uRxI2gdoAp7N2Y6LiBvyYpfDgPVy+i+BpYH180KZh+fz1TsBQOW4uwGXAJvVuR8AEdECeDEZMzOzDnBLjVnj+4D0cH5Mje1fJk1xfCewT2nbCGBAft0feCAfr10iYgbwfeBCSXsCS0fE1e09ThvneDqf47utZDsB+GUloMn73RIRo6rkfRD4FICkJYHDgWMiYlbe7zJSwLhTO4s6Cli3jTwH59akJyRtn8vQX9Kt+fXHJf01t/pcIumZSusZsLCk30qaLOlOSUtUO4GkIyW1SGp57e032lkFMzOzxuKgxqx3uAAYLGnZKtsGANeSAphBpW1PAh+XtHzedk1p+xKl7mcDqCEi/gK8BgwH/reVst5TOF6tQKyWscCGrWzfOOepxx7AH/PrdYFnI+LNUp6WfMz22Btoa22aRSJia+Bo4GdVtv8MuDsi+gE3AWsUtq0HXBARGwOvAwdWO0FEDIuI5ohoXqFPtY+FmZlZ7+HuZ2a9QES8KWk4qRVjRiVd0lbAyxHxjKTngN9LWj4i/lPYfSQwENgG+Ebp0PV2P6u4AFgiIh5vJU97up+VzdWXrWZGaUXgLmBJYFhEnJk3nSHpV8DKwLaF40aN80WNbZTSz5B0IvAycEQbxRuZf48hdY0r+xywP0BE3C6peL+mRcS4NvY3MzNboLilxqz3+A3pYXqpQtogYENJ04GpwDLM/c3+NaQxMX+NiA/nsQwf5p+u0heY0sr2yUA/gIh4NQdkw4A+hTzHkVpmTgSuyGlPAWsWJ03I+gGPAq8Cy5e2rQAUg7PjImKLiNg1ItoazD8z/55F9S+XWgveZhZe19rfzMxsgeKgxqyXiIjXgOvIrQR54P/BwGYR0RQRTaRJAQaV9nsWGAJcOF8L3E6SmoAzSRMB1PIrYEieHKFiyXKmHLydAywkafc8ecIVwK8lLZzPd2je9+6IeBt4sTJbmqQVSN3X7p/XetVwP/ClfK7dmDugMjMzswIHNWa9y1lAZUD5DsDzEVGczWwUsJGkVYs7RcQlETG1yvHKY2pO64QyFsfUDC+kXyLpufzzYE5bpzKlMylgO6/WzGe5HhOB7wHD85TODwCfBv5QJW8AvwCOz0k/Av4LPCHpSVJAuH/OB3AocKKkccDdwEk1rllnOAnYTdJYYE/gReCtLjqXmZlZw9Ps/6/NzKwnkLQYMCsiPshr3lzUzrFNc2hubo6WFs8WbWZm3U/SmIiYa722eeW+2GZmPc8awHW5C+F7wNe7uTxmZmY9moMaM2sXSTcBa5WST4iIO+ZjGQ4ndTMreiAijppfZWiLpAuA7UrJ57TWfa4iIp4kTYpgZmZmdXD3MzOzXm6LNdaLO0/4dZcdf+Wj9u6yY5uZWe/SVd3PPFGAmZmZmZk1NAc1ZmZmZmbW0BzUmJmZmZlZQ3NQY1YHSftLCkkb5vdN+f3PC3lWkvS+pPMlDSmsxTKr8Pq7rZzjUEmTJE2W9KikY3P65ZKm5f3HVxaAzNvulfR44fg35PShkp7PaU9KGilpo9J+zZJG5zzPSnq5cJymNq7HvZI6vT9sPSQtIukVSadWKdOzklRI+6OktyVtWqjba4Xr+bc2znWMpP9KWraQ1j/f+70LabdK6l8oR0thW7Oke/Prr0o6v0q5m/Pr6ZI+USjrvwr3cbykv0vas7DvlyTd3r4raGZm1vs4qDGrzyDSKu8DC2lPA3sV3h8MTAaIiFMiYou8tsiMyuuIOLfawfOD6tHAbhGxMdAPeKPub0FUAAANC0lEQVSQ5bh8rKOBi0u7Dy4c/6BC+tk5bT3gWuBuSR8v7hgR2+Tj/hS4tnCc6XVck+6yG/A48KViAJO9Tp5xTNJywKqQFuUs3I9byNczInZp41yDgIeB/UvpzwFDWtlv5WLw0U6zCmW9mNn3cXPgSODXkhaXtBRwCtBjZnwzMzPrLg5qzNogqQ/pQfkI5gxqZgBTCi0WA0ir3nfEj4BjI+IFgIj4b0T8tkq+B4FPtffgEXEtcCfw5Y4UTtISkq6RNEHStcASNfI1SbpP0tj889mc3l/SKEk35Vaoi/MaLOSWlLNy/rvKgVcVg4BzgGeBbUvbrmH2PToAGNmR+uZyrQP0AU7M5ywaD7whadcau5+R9+tUETEJ+BNwAvAzYHhETK2WV9KRkloktbz69hvVspiZmfUaDmrM2rYfcHtEPAG8JqlfYds1wEBJqwGzgBc6eI5NgDF15NsD+GMp7epCd6UzWtl3LLBhB8v3LeDdiNiM1DqwZY18LwG7RkQ/UpBXbJnaGvgBsCmwDinoAFgKGJv3+T/Sw3pVkpYAdgZuBUYwd7BxF7CDpIVJwc219VawikH5HPcBG0haubT9F9QOXB4EZkracR7OX8tJpOB0T+BXtTJFxLCIaI6I5hX7LFsrm5mZWa/goMasbYNIwQv5d/FB+nZg15w2Lw/QbTlD0tPAVcAvS9uK3c+Oa+UY5a5a7bFDPjcRMQGYUCPfosBvJU0Ergc2Kmx7KCKejohZpGDhczn9Q2Zfu6sK6dXsBdwTEe8CNwL75wCmYhapm+AAYIl57EY3ELgmIj4ktfgcXNwYEfcBSNq+xv7Vgp5aC4PVvWBYRLxDul5XRsTMevczMzPrzRzUmLVC0orATsClkqYDx5EemAUQEe+RWlh+QHrI7qjJ1G79IJ93XdJD8hUdPEdfYEo9GZUmRqi0/lS618314F0l3zHAv4HNgWbgY4Xs5f078oA/CNgl34sxwIpAuTXkGuA8Ot4VEEmbAesBf83nGsjcrUKQWq2qjq2JiLuBxZmzi9yrwPKlrCsAr7SziB/mHzMzM8NBjVlbDiKNW1gzIpoiYnVgGrBaIc9ZwAkR8eo8nOdU4FeSVgGQtJhKM6XlFoNzgIUk7d6eg0s6kDTAfkQ9+SPipkLrTwswChicj7UJsFmNfMsCL+ayHgIUW1G2lrRWHkszgNSiAunvUGWCgy8X0st1WIbUirNGvhdNpEHy5WDjPtL1rKuuNQwChlbOExGfBD4lac1ipoi4kxSkbF7jOKcAxxfePwxsV7jPzcBiwD/noaxmZmYLvEW6uwBmPdwg4LRS2o3AjytvImIyedazjoqIv0j6BPC3PKNXAL+vki8k/YL0oHxHTr5a0oz8+pXCjF7HSPoKaczKJGCniHi5g0W8CLhM0gRgHPBQjXwXAjdKOhi4B3insO1B0rXclBQk3ZTT3wE2ljSGNOPbgBrHPgC4u9Tl6mZSMLhYJSEiAjizHXWrZiBpzErRTTl9dCn9lFyOueT7+nLh/b8lfQ/4Sw7u3gYG5SDQzMzMOkjp/38zs66T13A5NiL2qrLt7YjoM/9LteBobm6OlpaWtjOamZl1MUljIqLT17pz9zMzMzMzM2tobqkxm48kDaE0ixZwfUSc0h3l6akkXUBeRLPgnIi4rJPPsylwZSl5ZkRs05nn6W6S3iItWNpbrET7J1fo6Vynnq+31Qdcp0bQ2+oDsEFELN3ZB3VQY2bWy0lq6Yqm/u7S2+oDrlMj6G31AdepEfS2+kDX1cndz8zMzMzMrKE5qDEzMzMzs4bmoMbMrPcb1t0F6GS9rT7gOjWC3lYfcJ0aQW+rD3RRnTymxszMzMzMGppbaszMzMzMrKE5qDEzMzMzs4bmoMbMrJeStIekxyU9JemH3V2e1khaXdI9kqZImizpezl9BUl/lfRk/r18Tpekc3PdJkjqVzjWYTn/k5IO66465bIsLOkRSbfm92tJGp3Ldq2kj+X0xfL7p/L2psIxfpTTH5e0e/fU5KOyLCfpBkmP5Xv1mUa+R5KOyZ+3SZJGSFq80e6RpN9LeknSpEJap90TSVtKmpj3OVeSuqlOZ+TP3QRJN0larrCt6vWv9Tew1j2e33UqbDtWUkhaKb/v8fepVn0kfSdf88mSflVI7/p7FBH+8Y9//OOfXvYDLAxMBdYGPgaMBzbq7nK1Ut5VgX759dLAE8BGwK+AH+b0HwKn59dfAG4DBGwLjM7pKwBP59/L59fLd2O9vg/8Abg1v78OGJhfXwx8K7/+X+Di/HogcG1+vVG+d4sBa+V7unA31ucK4Gv59ceA5Rr1HgGfAqYBSxTuzVcb7R4BOwD9gEmFtE67J8BDwGfyPrcBe3ZTnXYDFsmvTy/Uqer1p5W/gbXu8fyuU05fHbgDeAZYqVHuU417tCPwN2Cx/H7l+XmP3FJjZtY7bQ08FRFPR8R7wDXAvt1cppoi4sWIGJtfvwVMIT107kt6kCb/3i+/3hcYHsk/gOUkrQrsDvw1Il6LiP8AfwX2mI9V+Yik1YAvApfm9wJ2Am7IWcr1qdTzBmDnnH9f4JqImBkR04CnSPd2vpO0DOlB5ncAEfFeRLxOA98jYBFgCUmLAEsCL9Jg9ygiRgGvlZI75Z7kbctExIORni6HF47VZarVKSLujIgP8tt/AKsV6lTt+lf9G9jGv8MuU+M+AZwNHA8UZ+7q8fepRn2+BZwWETNznpcK9enye+Sgxsysd/oU8M/C++dyWo+Xu/X0BUYDn4iIFyEFPsDKOVut+vWkev+G9LDyYX6/IvB64cGsWLaPyp23v5Hz96T6rA28DFym1KXuUklL0aD3KCKeB84EniUFM28AY2jse1TRWffkU/l1Ob27/Q+pNQLaX6fW/h3OV5L2AZ6PiPGlTY16n9YHts/dxv5P0lY5fb7cIwc1Zma9U7X+1D1+Dn9JfYAbgaMj4s3WslZJi1bS5ytJewEvRcSYYnKVrNHGth5Rn2wRUneTiyKiL/AOqWtTLT26Tnmcyb6k7jCfBJYC9qyStZHuUVvaW4ceVzdJQ4APgKsrSVWy9fg6SVoSGAL8tNrmKmk9vk6kvxHLk7rMHQdcl1td5kt9HNSYmfVOz5H6alesBrzQTWWpi6RFSQHN1RExMif/O3etIP+udGeoVb+eUu/tgH0kTSd1qdiJ1HKzXO7qVC7bR+XO25clde3oKfUhl+W5iBid399ACnIa9R7tAkyLiJcj4n1gJPBZGvseVXTWPXmO2d28iundIg+M3wsYnLtZQfvr9Aq17/H8tA4poB6f/06sBoyVtAqNe5+eA0bmbnMPkVqpV2I+3SMHNWZmvdPDwHp5BpmPkQY239LNZaopf5v3O2BKRPy6sOkWoDLDz2HAzYX0Q/MsQdsCb+RuNncAu0laPn8Tv1tOm68i4kcRsVpENJGu/d0RMRi4BzgoZyvXp1LPg3L+yOkDlWbeWgtYjzQgeL6LiH8B/5S0QU7aGXiUBr1HpG5n20paMn/+KvVp2HtU0Cn3JG97S9K2+RodWjjWfCVpD+AEYJ+IeLewqdb1r/o3MN+zWvd4vomIiRGxckQ05b8Tz5EmS/kXjXuf/kj6AgdJ65MG/7/C/LpHbc0k4B//+Mc//mnMH9IMOk+QZpcZ0t3laaOsnyN1L5gAjMs/XyD1rb4LeDL/XiHnF3BBrttEoLlwrP8hDUR9Cji8B9StP7NnP1s7/2f+FHA9s2cJWjy/fypvX7uw/5Bcz8eZDzNPtVGXLYCWfJ/+SOpq0rD3CDgJeAyYBFxJmp2poe4RMII0Juh90oPxEZ15T4DmfH2mAucD6qY6PUUaf1H5+3BxW9efGn8Da93j+V2n0vbpzJ79rMffpxr36GPAVbkcY4Gd5uc9Ut7RzMzMzMysIbn7mZmZmZmZNTQHNWZmZmZm1tAc1JiZmZmZWUNzUGNmZmZmZg3NQY2ZmZmZmTU0BzVmZmZmdZL0TUmHdnc5zGxOntLZzMzMzMwa2iLdXQAzMzOz7iTpJ8Bg0uKOrwBjgDeAI0kLCj4FHBIR70oaCrwdEWdKuhcYDewILEdaUPG++V8DM3P3MzMzM1tgSWoGDgT6AgeQVmYHGBkRW0XE5sAU0orp1SwSEVsDRwM/6+rymll1bqkxMzOzBdnngJsjYgaApD/l9E0k/YLUAtMHuKPG/iPz7zFAUxeW08xa4ZYaMzMzW5CpRvrlwLcjYlPgJGDxGvlm5t+z8JfFZt3GQY2ZmZktyO4H9pa0uKQ+wBdz+tLAi5IWJY23MbMezN8omJmZ2QIrIh6WdAswHngGaCFNEvAT0iQAzwATSUGOmfVQntLZzMzMFmiS+kTE25KWBEYBR0bE2O4ul5nVzy01ZmZmtqAbJmkj0riZKxzQmDUet9SYmZmZmVlD80QBZmZmZmbW0BzUmJmZmZlZQ3NQY2ZmZmZmDc1BjZmZmZmZNTQHNWZmZmZm1tD+Hz/6o/tcuvyBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e11118ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ex.pltImp(imp); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:44:16.691385Z",
     "start_time": "2018-07-12T06:44:16.667390Z"
    }
   },
   "outputs": [],
   "source": [
    "def feature_vs_y(feature, y, feature_name=None, kind='kde', bw_method=None, bins=100):\n",
    "    \n",
    "    if feature_name is None:\n",
    "        feature_name = feature.name\n",
    "    \n",
    "    df = y.to_frame('y')\n",
    "    df[feature_name] = feature\n",
    "    y0 = df[df.y==0]\n",
    "    y1 = df[df.y==1]\n",
    "    y0[feature_name].name = 'Actual:0'\n",
    "    y1[feature_name].name = 'Actual:1'\n",
    "    if kind=='kde':\n",
    "        y0[feature_name].plot(kind='kde', legend=True, bw_method=bw_method)\n",
    "        y1[feature_name].plot(kind='kde', legend=True, bw_method=bw_method)\n",
    "    elif kind=='hist':\n",
    "        y0[feature_name].plot(kind='hist',legend=True, bins=bins)\n",
    "        y1[feature_name].plot(kind='hist',legend=True, bins=bins)\n",
    "        \n",
    "    plt.xlabel(f'{feature_name}')\n",
    "    plt.title(f'{feature_name} Distribution', fontsize=13, alpha=0.5)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:47:41.934026Z",
     "start_time": "2018-07-12T06:47:11.093970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e128ccac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_vs_y(X_tr['AMT_GOODS_PRICE-d-total_debt'], tmp.y, bw_method=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T06:46:54.556678Z",
     "start_time": "2018-07-12T06:46:54.077457Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8dfa7c42e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_vs_y(X_tr['AMT_GOODS_PRICE-d-total_debt'], tmp.y, kind='hist', bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T07:46:43.973245Z",
     "start_time": "2018-07-12T07:46:43.969537Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1413701, 439)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T07:47:34.084064Z",
     "start_time": "2018-07-12T07:47:33.066872Z"
    }
   },
   "outputs": [],
   "source": [
    "X_te = prev_te.drop(['SK_ID_PREV', 'SK_ID_CURR', 'TARGET'], axis=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T07:47:41.701690Z",
     "start_time": "2018-07-12T07:47:41.698125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256513, 439)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_te.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T07:55:58.960534Z",
     "start_time": "2018-07-12T07:55:58.949494Z"
    }
   },
   "outputs": [],
   "source": [
    "tmp['y_pred'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T07:55:59.282171Z",
     "start_time": "2018-07-12T07:55:59.186218Z"
    }
   },
   "outputs": [],
   "source": [
    "sub = prev_te[['SK_ID_PREV', 'SK_ID_CURR']]\n",
    "sub['y_pred'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:02:23.754598Z",
     "start_time": "2018-07-12T07:55:59.605498Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds.\n",
      "[50]\ttrain's auc: 0.688205\tvalid's auc: 0.655653\n",
      "[100]\ttrain's auc: 0.705659\tvalid's auc: 0.656727\n",
      "[150]\ttrain's auc: 0.717896\tvalid's auc: 0.656251\n",
      "[200]\ttrain's auc: 0.730468\tvalid's auc: 0.655952\n",
      "Early stopping, best iteration is:\n",
      "[106]\ttrain's auc: 0.707368\tvalid's auc: 0.656787\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[50]\ttrain's auc: 0.688276\tvalid's auc: 0.647237\n",
      "[100]\ttrain's auc: 0.705566\tvalid's auc: 0.648866\n",
      "[150]\ttrain's auc: 0.718293\tvalid's auc: 0.648071\n",
      "Early stopping, best iteration is:\n",
      "[93]\ttrain's auc: 0.703649\tvalid's auc: 0.648921\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[50]\ttrain's auc: 0.685824\tvalid's auc: 0.652476\n",
      "[100]\ttrain's auc: 0.707254\tvalid's auc: 0.653873\n",
      "[150]\ttrain's auc: 0.720785\tvalid's auc: 0.653365\n",
      "Early stopping, best iteration is:\n",
      "[92]\ttrain's auc: 0.703899\tvalid's auc: 0.654141\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[50]\ttrain's auc: 0.686929\tvalid's auc: 0.653819\n",
      "[100]\ttrain's auc: 0.704392\tvalid's auc: 0.655052\n",
      "[150]\ttrain's auc: 0.718752\tvalid's auc: 0.655543\n",
      "[200]\ttrain's auc: 0.729979\tvalid's auc: 0.655243\n",
      "Early stopping, best iteration is:\n",
      "[146]\ttrain's auc: 0.717652\tvalid's auc: 0.655712\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[50]\ttrain's auc: 0.68589\tvalid's auc: 0.653367\n",
      "[100]\ttrain's auc: 0.703296\tvalid's auc: 0.655553\n",
      "[150]\ttrain's auc: 0.717601\tvalid's auc: 0.655055\n",
      "[200]\ttrain's auc: 0.728163\tvalid's auc: 0.654653\n",
      "Early stopping, best iteration is:\n",
      "[116]\ttrain's auc: 0.707572\tvalid's auc: 0.655622\n"
     ]
    }
   ],
   "source": [
    "for train_index, test_index in group_kfold.split(X_tr, tmp.y, tmp.g):\n",
    "    dtrain = lgb.Dataset(X_tr.iloc[train_index], tmp.iloc[train_index].y)\n",
    "    dvalid = lgb.Dataset(X_tr.iloc[test_index], tmp.iloc[test_index].y)\n",
    "    \n",
    "    model = lgb.train(params=param, train_set=dtrain, num_boost_round=9999, \n",
    "                  valid_sets=[dtrain, dvalid], \n",
    "                  valid_names=['train','valid'], \n",
    "                  early_stopping_rounds=100, \n",
    "                  #evals_result=evals_result, \n",
    "                  verbose_eval=50\n",
    "                  )\n",
    "    \n",
    "    tmp.iloc[test_index, -1] = model.predict(X_tr.iloc[test_index])\n",
    "    sub['y_pred'] += model.predict(X_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:11:13.365941Z",
     "start_time": "2018-07-12T08:11:13.309010Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    256513.000000\n",
       "mean          0.434545\n",
       "std           0.225604\n",
       "min           0.021394\n",
       "25%           0.277965\n",
       "50%           0.420130\n",
       "75%           0.528173\n",
       "max           2.666075\n",
       "Name: y_pred, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.y_pred.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:16:58.021105Z",
     "start_time": "2018-07-12T08:16:57.529056Z"
    }
   },
   "outputs": [],
   "source": [
    "sub.y_pred /= 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:17:31.215795Z",
     "start_time": "2018-07-12T08:17:31.207768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_PREV</th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1397919</td>\n",
       "      <td>321676</td>\n",
       "      <td>0.124594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2273188</td>\n",
       "      <td>270658</td>\n",
       "      <td>0.083100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1285768</td>\n",
       "      <td>142748</td>\n",
       "      <td>0.080824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2536650</td>\n",
       "      <td>338725</td>\n",
       "      <td>0.070441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2191093</td>\n",
       "      <td>182450</td>\n",
       "      <td>0.064916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_PREV  SK_ID_CURR    y_pred\n",
       "0     1397919      321676  0.124594\n",
       "1     2273188      270658  0.083100\n",
       "2     1285768      142748  0.080824\n",
       "3     2536650      338725  0.070441\n",
       "4     2191093      182450  0.064916"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:17:35.302334Z",
     "start_time": "2018-07-12T08:17:35.292046Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>y</th>\n",
       "      <th>cnt</th>\n",
       "      <th>w</th>\n",
       "      <th>g</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>271877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2</td>\n",
       "      <td>0.059995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>4</td>\n",
       "      <td>0.038191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>122040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.122890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>3</td>\n",
       "      <td>0.048063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.165824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR    y  cnt         w  g    y_pred\n",
       "0      271877  0.0    3  0.333333  2  0.059995\n",
       "1      108129  0.0    6  0.166667  4  0.038191\n",
       "2      122040  0.0    4  0.250000  0  0.122890\n",
       "3      176158  0.0   23  0.043478  3  0.048063\n",
       "4      202054  0.0   25  0.040000  4  0.165824"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:18:55.280347Z",
     "start_time": "2018-07-12T08:18:55.256309Z"
    }
   },
   "outputs": [],
   "source": [
    "sub_train = tmp.copy()\n",
    "sub_test = sub.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.merge(train, sub_train, on='', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:24:29.743356Z",
     "start_time": "2018-07-12T08:24:26.588988Z"
    }
   },
   "outputs": [],
   "source": [
    "trte = pd.concat([train, test], ignore_index=True)\n",
    "col = [c for c in trte.columns if trte[c].dtype=='O']\n",
    "trte = pd.get_dummies(trte, columns=col, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:26:58.151136Z",
     "start_time": "2018-07-12T08:26:55.931939Z"
    }
   },
   "outputs": [],
   "source": [
    "train = trte[~trte.TARGET.isnull()].reset_index(drop=True)\n",
    "test = trte[trte.TARGET.isnull()].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:26:58.155969Z",
     "start_time": "2018-07-12T08:26:58.152542Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((307511, 357), (48744, 357))"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:29:18.402990Z",
     "start_time": "2018-07-12T08:29:18.395951Z"
    }
   },
   "outputs": [],
   "source": [
    "SEED = 71\n",
    "\n",
    "param = {\n",
    "         'objective': 'binary',\n",
    "         'metric': 'auc',\n",
    "         'learning_rate': 0.02,\n",
    "         'max_depth': 6,\n",
    "         'num_leaves': 63,\n",
    "         'max_bin': 255,\n",
    "         \n",
    "         'min_child_weight': 10,\n",
    "         'min_data_in_leaf': 150,\n",
    "         'reg_lambda': 0.5,  # L2 regularization term on weights.\n",
    "         'reg_alpha': 0.5,  # L1 regularization term on weights.\n",
    "         \n",
    "         'colsample_bytree': 0.9,\n",
    "         'subsample': 0.9,\n",
    "#         'nthread': 32,\n",
    "         'nthread': 64,\n",
    "         'bagging_freq': 1,\n",
    "         'verbose':-1,\n",
    "         'seed': SEED\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:28:07.135708Z",
     "start_time": "2018-07-12T08:28:06.730934Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train = train.drop(['SK_ID_CURR', 'TARGET'], axis=1)\n",
    "y_train = train.TARGET\n",
    "dtrain = lgb.Dataset(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:31:33.141779Z",
     "start_time": "2018-07-12T08:29:19.018577Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50]\tcv_agg's auc: 0.74507 + 0.00114953\n",
      "[100]\tcv_agg's auc: 0.751139 + 0.00151758\n",
      "[150]\tcv_agg's auc: 0.755801 + 0.00178092\n",
      "[200]\tcv_agg's auc: 0.759216 + 0.00197162\n",
      "[250]\tcv_agg's auc: 0.761461 + 0.00197868\n",
      "[300]\tcv_agg's auc: 0.763139 + 0.00201062\n",
      "[350]\tcv_agg's auc: 0.764298 + 0.00196472\n",
      "[400]\tcv_agg's auc: 0.765227 + 0.00198344\n",
      "[450]\tcv_agg's auc: 0.765903 + 0.00192911\n",
      "[500]\tcv_agg's auc: 0.766482 + 0.00197955\n",
      "[550]\tcv_agg's auc: 0.766972 + 0.00193116\n",
      "[600]\tcv_agg's auc: 0.767358 + 0.00193461\n",
      "[650]\tcv_agg's auc: 0.767667 + 0.00194496\n",
      "[700]\tcv_agg's auc: 0.767976 + 0.00196486\n",
      "[750]\tcv_agg's auc: 0.768177 + 0.00192883\n",
      "[800]\tcv_agg's auc: 0.768361 + 0.0019918\n",
      "[850]\tcv_agg's auc: 0.76855 + 0.00197065\n",
      "[900]\tcv_agg's auc: 0.768706 + 0.00201259\n",
      "[950]\tcv_agg's auc: 0.768741 + 0.0019791\n",
      "[1000]\tcv_agg's auc: 0.768867 + 0.00196327\n",
      "[1050]\tcv_agg's auc: 0.768974 + 0.00200703\n",
      "[1100]\tcv_agg's auc: 0.769085 + 0.00201907\n",
      "[1150]\tcv_agg's auc: 0.769143 + 0.00207422\n",
      "[1200]\tcv_agg's auc: 0.769126 + 0.00210641\n",
      "[1250]\tcv_agg's auc: 0.769116 + 0.00209958\n"
     ]
    }
   ],
   "source": [
    "ret = lgb.cv(param, dtrain, 9999, \n",
    "             early_stopping_rounds=100, verbose_eval=50,\n",
    "             seed=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:38:55.718776Z",
     "start_time": "2018-07-12T08:36:37.468631Z"
    }
   },
   "outputs": [],
   "source": [
    "train_ = train.set_index('SK_ID_CURR')\n",
    "gr = sub_train.groupby('SK_ID_CURR')\n",
    "train_['f1'] = gr.y_pred.min()\n",
    "train_['f2'] = gr.y_pred.mean()\n",
    "train_['f3'] = gr.y_pred.max()\n",
    "train_['f4'] = gr.y_pred.var()\n",
    "train_['f5'] = gr.y_pred.median()\n",
    "train_['f6'] = gr.y_pred.quantile(.25)\n",
    "train_['f7'] = gr.y_pred.quantile(.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:56:32.759225Z",
     "start_time": "2018-07-12T08:56:09.827171Z"
    }
   },
   "outputs": [],
   "source": [
    "test_ = test.set_index('SK_ID_CURR')\n",
    "gr = sub_test.groupby('SK_ID_CURR')\n",
    "test_['f1'] = gr.y_pred.min()\n",
    "test_['f2'] = gr.y_pred.mean()\n",
    "test_['f3'] = gr.y_pred.max()\n",
    "test_['f4'] = gr.y_pred.var()\n",
    "test_['f5'] = gr.y_pred.median()\n",
    "test_['f6'] = gr.y_pred.quantile(.25)\n",
    "test_['f7'] = gr.y_pred.quantile(.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:42:04.094594Z",
     "start_time": "2018-07-12T08:39:34.534141Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50]\tcv_agg's auc: 0.759214 + 0.0027898\n",
      "[100]\tcv_agg's auc: 0.762732 + 0.00285093\n",
      "[150]\tcv_agg's auc: 0.766171 + 0.00290945\n",
      "[200]\tcv_agg's auc: 0.768694 + 0.00274527\n",
      "[250]\tcv_agg's auc: 0.770277 + 0.00282348\n",
      "[300]\tcv_agg's auc: 0.771344 + 0.00276887\n",
      "[350]\tcv_agg's auc: 0.772218 + 0.00275912\n",
      "[400]\tcv_agg's auc: 0.772853 + 0.00268184\n",
      "[450]\tcv_agg's auc: 0.773281 + 0.00266055\n",
      "[500]\tcv_agg's auc: 0.773613 + 0.0026648\n",
      "[550]\tcv_agg's auc: 0.773924 + 0.00265154\n",
      "[600]\tcv_agg's auc: 0.77419 + 0.00264302\n",
      "[650]\tcv_agg's auc: 0.774429 + 0.00272744\n",
      "[700]\tcv_agg's auc: 0.77469 + 0.00279999\n",
      "[750]\tcv_agg's auc: 0.774864 + 0.00280513\n",
      "[800]\tcv_agg's auc: 0.775 + 0.00279346\n",
      "[850]\tcv_agg's auc: 0.775088 + 0.00283989\n",
      "[900]\tcv_agg's auc: 0.775201 + 0.00283367\n",
      "[950]\tcv_agg's auc: 0.77527 + 0.0027767\n",
      "[1000]\tcv_agg's auc: 0.775345 + 0.00276767\n",
      "[1050]\tcv_agg's auc: 0.775463 + 0.00274461\n",
      "[1100]\tcv_agg's auc: 0.775461 + 0.00278953\n",
      "[1150]\tcv_agg's auc: 0.77549 + 0.00277753\n",
      "[1200]\tcv_agg's auc: 0.775501 + 0.00279567\n",
      "[1250]\tcv_agg's auc: 0.7755 + 0.00282418\n",
      "[1300]\tcv_agg's auc: 0.775487 + 0.00279494\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "X_train = train_.reset_index().drop(['SK_ID_CURR', 'TARGET'], axis=1)\n",
    "y_train = train_.TARGET\n",
    "dtrain = lgb.Dataset(X_train, y_train)\n",
    "\n",
    "ret = lgb.cv(param, dtrain, 9999, \n",
    "             early_stopping_rounds=100, verbose_eval=50,\n",
    "             seed=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:46:01.252821Z",
     "start_time": "2018-07-12T08:45:57.379239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e11ef3e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = lgb.train(param, dtrain, 100)\n",
    "imp = ex.getImp(model)\n",
    "ex.pltImp(imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T08:59:57.632456Z",
     "start_time": "2018-07-12T08:59:57.301411Z"
    }
   },
   "outputs": [],
   "source": [
    "train_[['f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7']].reset_index().to_feather('../feature_someone/train_prev.f')\n",
    "test_[['f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7']].reset_index().to_feather('../feature_someone/test_prev.f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-12T10:28:24.824203Z",
     "start_time": "2018-07-12T10:28:24.819283Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment',\n",
       " 'NAME_CASH_LOAN_PURPOSE_Refusal to name the goal',\n",
       " 'NAME_CASH_LOAN_PURPOSE_Repairs',\n",
       " 'NAME_CASH_LOAN_PURPOSE_Urgent needs',\n",
       " 'NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday',\n",
       " 'NAME_CASH_LOAN_PURPOSE_XAP',\n",
       " 'NAME_CASH_LOAN_PURPOSE_XNA',\n",
       " 'NAME_CONTRACT_STATUS_Canceled',\n",
       " 'NAME_CONTRACT_STATUS_Refused',\n",
       " 'NAME_CONTRACT_STATUS_Unused offer',\n",
       " 'NAME_PAYMENT_TYPE_Cashless from the account of the employer',\n",
       " 'NAME_PAYMENT_TYPE_Non-cash from your account',\n",
       " 'NAME_PAYMENT_TYPE_XNA',\n",
       " 'CODE_REJECT_REASON_HC',\n",
       " 'CODE_REJECT_REASON_LIMIT',\n",
       " 'CODE_REJECT_REASON_SCO',\n",
       " 'CODE_REJECT_REASON_SCOFR',\n",
       " 'CODE_REJECT_REASON_SYSTEM',\n",
       " 'CODE_REJECT_REASON_VERIF',\n",
       " 'CODE_REJECT_REASON_XAP',\n",
       " 'CODE_REJECT_REASON_XNA',\n",
       " 'NAME_TYPE_SUITE_Family',\n",
       " 'NAME_TYPE_SUITE_Group of people',\n",
       " 'NAME_TYPE_SUITE_Other_A',\n",
       " 'NAME_TYPE_SUITE_Other_B',\n",
       " 'NAME_TYPE_SUITE_Spouse, partner',\n",
       " 'NAME_TYPE_SUITE_Unaccompanied',\n",
       " 'NAME_CLIENT_TYPE_Refreshed',\n",
       " 'NAME_CLIENT_TYPE_Repeater',\n",
       " 'NAME_CLIENT_TYPE_XNA',\n",
       " 'NAME_GOODS_CATEGORY_Animals',\n",
       " 'NAME_GOODS_CATEGORY_Audio/Video',\n",
       " 'NAME_GOODS_CATEGORY_Auto Accessories',\n",
       " 'NAME_GOODS_CATEGORY_Clothing and Accessories',\n",
       " 'NAME_GOODS_CATEGORY_Computers',\n",
       " 'NAME_GOODS_CATEGORY_Construction Materials',\n",
       " 'NAME_GOODS_CATEGORY_Consumer Electronics',\n",
       " 'NAME_GOODS_CATEGORY_Direct Sales',\n",
       " 'NAME_GOODS_CATEGORY_Education',\n",
       " 'NAME_GOODS_CATEGORY_Fitness',\n",
       " 'NAME_GOODS_CATEGORY_Furniture',\n",
       " 'NAME_GOODS_CATEGORY_Gardening',\n",
       " 'NAME_GOODS_CATEGORY_Homewares',\n",
       " 'NAME_GOODS_CATEGORY_House Construction',\n",
       " 'NAME_GOODS_CATEGORY_Insurance',\n",
       " 'NAME_GOODS_CATEGORY_Jewelry',\n",
       " 'NAME_GOODS_CATEGORY_Medical Supplies',\n",
       " 'NAME_GOODS_CATEGORY_Medicine',\n",
       " 'NAME_GOODS_CATEGORY_Mobile',\n",
       " 'NAME_GOODS_CATEGORY_Office Appliances',\n",
       " 'NAME_GOODS_CATEGORY_Other',\n",
       " 'NAME_GOODS_CATEGORY_Photo / Cinema Equipment',\n",
       " 'NAME_GOODS_CATEGORY_Sport and Leisure',\n",
       " 'NAME_GOODS_CATEGORY_Tourism',\n",
       " 'NAME_GOODS_CATEGORY_Vehicles',\n",
       " 'NAME_GOODS_CATEGORY_Weapon',\n",
       " 'NAME_GOODS_CATEGORY_XNA',\n",
       " 'NAME_PORTFOLIO_Cars',\n",
       " 'NAME_PORTFOLIO_Cash',\n",
       " 'NAME_PORTFOLIO_POS',\n",
       " 'NAME_PORTFOLIO_XNA',\n",
       " 'NAME_PRODUCT_TYPE_walk-in',\n",
       " 'NAME_PRODUCT_TYPE_x-sell',\n",
       " 'CHANNEL_TYPE_Car dealer',\n",
       " 'CHANNEL_TYPE_Channel of corporate sales',\n",
       " 'CHANNEL_TYPE_Contact center',\n",
       " 'CHANNEL_TYPE_Country-wide',\n",
       " 'CHANNEL_TYPE_Credit and cash offices',\n",
       " 'CHANNEL_TYPE_Regional / Local',\n",
       " 'CHANNEL_TYPE_Stone',\n",
       " 'NAME_SELLER_INDUSTRY_Clothing',\n",
       " 'NAME_SELLER_INDUSTRY_Connectivity',\n",
       " 'NAME_SELLER_INDUSTRY_Construction',\n",
       " 'NAME_SELLER_INDUSTRY_Consumer electronics',\n",
       " 'NAME_SELLER_INDUSTRY_Furniture',\n",
       " 'NAME_SELLER_INDUSTRY_Industry',\n",
       " 'NAME_SELLER_INDUSTRY_Jewelry',\n",
       " 'NAME_SELLER_INDUSTRY_MLM partners',\n",
       " 'NAME_SELLER_INDUSTRY_Tourism',\n",
       " 'NAME_SELLER_INDUSTRY_XNA',\n",
       " 'NAME_YIELD_GROUP_high',\n",
       " 'NAME_YIELD_GROUP_low_action',\n",
       " 'NAME_YIELD_GROUP_low_normal',\n",
       " 'NAME_YIELD_GROUP_middle',\n",
       " 'PRODUCT_COMBINATION_Card X-Sell',\n",
       " 'PRODUCT_COMBINATION_Cash',\n",
       " 'PRODUCT_COMBINATION_Cash Street: high',\n",
       " 'PRODUCT_COMBINATION_Cash Street: low',\n",
       " 'PRODUCT_COMBINATION_Cash Street: middle',\n",
       " 'PRODUCT_COMBINATION_Cash X-Sell: high',\n",
       " 'PRODUCT_COMBINATION_Cash X-Sell: low',\n",
       " 'PRODUCT_COMBINATION_Cash X-Sell: middle',\n",
       " 'PRODUCT_COMBINATION_POS household with interest',\n",
       " 'PRODUCT_COMBINATION_POS household without interest',\n",
       " 'PRODUCT_COMBINATION_POS industry with interest',\n",
       " 'PRODUCT_COMBINATION_POS industry without interest',\n",
       " 'PRODUCT_COMBINATION_POS mobile with interest',\n",
       " 'PRODUCT_COMBINATION_POS mobile without interest',\n",
       " 'PRODUCT_COMBINATION_POS other with interest',\n",
       " 'PRODUCT_COMBINATION_POS others without interest']"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev.columns.tolist()[-100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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